http://2013.igem.org/wiki/index.php?title=Special:Contributions/USTCkun&feed=atom&limit=50&target=USTCkun&year=&month=2013.igem.org - User contributions [en]2024-03-29T13:59:27ZFrom 2013.igem.orgMediaWiki 1.16.5http://2013.igem.org/Team:USTC-Software/Project/ExamplesTeam:USTC-Software/Project/Examples2013-10-28T03:26:39Z<p>USTCkun: </p>
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<li><a href="#tvel" class="button">Test and verify by experiment literatures</a></br><br />
<a href="#gge" class="button" id="subbutton">IHF+/IHF-</a></br><br />
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<h1>Examples</h1><br />
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<h2 id="tvel">Test and verify by experiment literatures</h2><br />
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<p align="justify">To prove our software’s reliability, we search for lots of literatures. It is hard to find an appropriate literature which research the effect of importing an exogenous gene into E.coli K-12. But actually, our software could also simulate the effect of changing endogenous gene by putting the same promoter and gene sequence in.</br></br><br />
So, we eventually found four literatures to test and verify our software.<br />
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<h3 id="gge">Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF INTEGRATION HOST FACTOR</h3><br />
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<p align="justify">In this literature, Stuart and his team measure the gene expression profiles in otherwise isogenic integration host factor IHF+ and IHF- strains. And IHF is one of the genes in our genetic regulatory network(GRN). </br></br><br />
By importing the IHF’s promoter and gene sequence, we used our software simulating the enhancement of IHF’s expression and compared the result with the gene expression profile in that literature.</br></br><br />
There are 30 genes in that profile which are also in our GRN. Here is the list and Genes differentially expressed between E. coli K12 strains IH100 (IHF+) and IH105 (IHF-) with a p value less than 0.0005:<br />
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<table width="1000px" border="0" cellspacing="0" cellpadding="0" style="border-top:2px #000000 solid;border-bottom:2px #000000 solid;background: url(https://static.igem.org/mediawiki/2013/1/1e/M_1243691170517.jpg) repeat;"><br />
<tr><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Gene</p></td><br />
<td width="137" colspan="2" style="border-bottom:2px #000 solid;"><p align="center">Avg</p></td><br />
<td width="137" colspan="2" style="border-bottom:2px #000 solid;"><p align="center">S.D.</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">p value</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Fold</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Compare<br /><br />
Result</p></td><br />
</tr><br />
<tr><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH100</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH105</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH100</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH105</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>glnA</em></p></td><br />
<td width="69"><p align="center">2.91E-03</p></td><br />
<td width="69"><p align="center">9.39E-04</p></td><br />
<td width="69"><p align="center">6.80E-04</p></td><br />
<td width="69"><p align="center">1.33E-04</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">-3.1</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvA</em></p></td><br />
<td width="69"><p align="center">5.06E-04</p></td><br />
<td width="69"><p align="center">3.42E-04</p></td><br />
<td width="69"><p align="center">1.86E-05</p></td><br />
<td width="69"><p align="center">2.26E-05</p></td><br />
<td width="69"><p align="center">3.00E-05</p></td><br />
<td width="69"><p align="center">-1.48</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvE</em></p></td><br />
<td width="69"><p align="center">5.81E-04</p></td><br />
<td width="69"><p align="center">3.58E-04</p></td><br />
<td width="69"><p align="center">4.70E-05</p></td><br />
<td width="69"><p align="center">5.77E-05</p></td><br />
<td width="69"><p align="center">9.80E-04</p></td><br />
<td width="69"><p align="center">-1.62</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvG</em></p></td><br />
<td width="69"><p align="center">1.97E-04</p></td><br />
<td width="69"><p align="center">7.67E-05</p></td><br />
<td width="69"><p align="center">2.65E-05</p></td><br />
<td width="69"><p align="center">2.23E-05</p></td><br />
<td width="69"><p align="center">4.40E-04</p></td><br />
<td width="69"><p align="center">-2.57</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>leuA</em></p></td><br />
<td width="69"><p align="center">6.99E-04</p></td><br />
<td width="69"><p align="center">1.07E-03</p></td><br />
<td width="69"><p align="center">9.21E-05</p></td><br />
<td width="69"><p align="center">9.23E-05</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">1.53</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cobT</em></p></td><br />
<td width="69"><p align="center">1.00E-05</p></td><br />
<td width="69"><p align="center">7.97E-05</p></td><br />
<td width="69"><p align="center">7.82E-06</p></td><br />
<td width="69"><p align="center">2.13E-05</p></td><br />
<td width="69"><p align="center">8.50E-04</p></td><br />
<td width="69"><p align="center">7.97</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cobU</em></p></td><br />
<td width="69"><p align="center">4.26E-05</p></td><br />
<td width="69"><p align="center">1.22E-04</p></td><br />
<td width="69"><p align="center">1.79E-05</p></td><br />
<td width="69"><p align="center">1.95E-05</p></td><br />
<td width="69"><p align="center">9.90E-04</p></td><br />
<td width="69"><p align="center">2.85</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacA</em></p></td><br />
<td width="69"><p align="center">5.14E-03</p></td><br />
<td width="69"><p align="center">1.21E-03</p></td><br />
<td width="69"><p align="center">1.54E-03</p></td><br />
<td width="69"><p align="center">3.52E-04</p></td><br />
<td width="69"><p align="center">2.50E-03</p></td><br />
<td width="69"><p align="center">-4.24</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacZ</em></p></td><br />
<td width="69"><p align="center">2.10E-03</p></td><br />
<td width="69"><p align="center">5.14E-04</p></td><br />
<td width="69"><p align="center">3.77E-04</p></td><br />
<td width="69"><p align="center">1.34E-04</p></td><br />
<td width="69"><p align="center">2.20E-04</p></td><br />
<td width="69"><p align="center">-4.08</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacY</em></p></td><br />
<td width="69"><p align="center">1.62E-03</p></td><br />
<td width="69"><p align="center">4.08E-04</p></td><br />
<td width="69"><p align="center">2.53E-04</p></td><br />
<td width="69"><p align="center">7.95E-05</p></td><br />
<td width="69"><p align="center">9.80E-05</p></td><br />
<td width="69"><p align="center">-3.96</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ompF</em></p></td><br />
<td width="69"><p align="center">7.23E-03</p></td><br />
<td width="69"><p align="center">2.35E-03</p></td><br />
<td width="69"><p align="center">1.90E-03</p></td><br />
<td width="69"><p align="center">3.69E-04</p></td><br />
<td width="69"><p align="center">2.40E-03</p></td><br />
<td width="69"><p align="center">-3.07</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>gltD</em></p></td><br />
<td width="69"><p align="center">9.91E-04</p></td><br />
<td width="69"><p align="center">1.40E-04</p></td><br />
<td width="69"><p align="center">1.88E-04</p></td><br />
<td width="69"><p align="center">3.06E-05</p></td><br />
<td width="69"><p align="center">1.10E-04</p></td><br />
<td width="69"><p align="center">-7.1</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lpdA</em></p></td><br />
<td width="69"><p align="center">1.07E-03</p></td><br />
<td width="69"><p align="center">7.60E-04</p></td><br />
<td width="69"><p align="center">1.17E-04</p></td><br />
<td width="69"><p align="center">7.75E-05</p></td><br />
<td width="69"><p align="center">4.60E-03</p></td><br />
<td width="69"><p align="center">-1.41</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>rffT</em></p></td><br />
<td width="69"><p align="center">5.81E-06</p></td><br />
<td width="69"><p align="center">3.65E-05</p></td><br />
<td width="69"><p align="center">4.66E-06</p></td><br />
<td width="69"><p align="center">2.86E-05</p></td><br />
<td width="69"><p align="center">9.40E-04</p></td><br />
<td width="69"><p align="center">6.28</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ndh</em></p></td><br />
<td width="69"><p align="center">5.03E-05</p></td><br />
<td width="69"><p align="center">1.46E-04</p></td><br />
<td width="69"><p align="center">1.94E-05</p></td><br />
<td width="69"><p align="center">3.29E-05</p></td><br />
<td width="69"><p align="center">2.50E-03</p></td><br />
<td width="69"><p align="center">2.9</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cheR</em></p></td><br />
<td width="69"><p align="center">1.29E-04</p></td><br />
<td width="69"><p align="center">2.68E-05</p></td><br />
<td width="69"><p align="center">2.07E-04</p></td><br />
<td width="69"><p align="center">1.75E-05</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">-4.82</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>sodA</em></p></td><br />
<td width="69"><p align="center">3.80E-04</p></td><br />
<td width="69"><p align="center">9.74E-04</p></td><br />
<td width="69"><p align="center">1.06E-04</p></td><br />
<td width="69"><p align="center">6.26E-05</p></td><br />
<td width="69"><p align="center">7.00E-05</p></td><br />
<td width="69"><p align="center">2.57</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>sodB</em></p></td><br />
<td width="69"><p align="center">7.80E-04</p></td><br />
<td width="69"><p align="center">1.91E-03</p></td><br />
<td width="69"><p align="center">2.45E-04</p></td><br />
<td width="69"><p align="center">4.11E-04</p></td><br />
<td width="69"><p align="center">3.30E-03</p></td><br />
<td width="69"><p align="center">2.44</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cpdB</em></p></td><br />
<td width="69"><p align="center">1.92E-05</p></td><br />
<td width="69"><p align="center">7.56E-05</p></td><br />
<td width="69"><p align="center">1.24E-05</p></td><br />
<td width="69"><p align="center">1.40E-05</p></td><br />
<td width="69"><p align="center">9.50E-04</p></td><br />
<td width="69"><p align="center">3.94</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>guaA</em></p></td><br />
<td width="69"><p align="center">8.25E-04</p></td><br />
<td width="69"><p align="center">4.31E-04</p></td><br />
<td width="69"><p align="center">5.43E-05</p></td><br />
<td width="69"><p align="center">1.34E204</p></td><br />
<td width="69"><p align="center">1.60E203</p></td><br />
<td width="69"><p align="center">-1.91</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>yiaJ</em></p></td><br />
<td width="69"><p align="center">3.47E-05</p></td><br />
<td width="69"><p align="center">6.15E-04</p></td><br />
<td width="69"><p align="center">1.74E-05</p></td><br />
<td width="69"><p align="center">1.64E204</p></td><br />
<td width="69"><p align="center">4.10E204</p></td><br />
<td width="69"><p align="center">17.74</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>dsdX</em></p></td><br />
<td width="69"><p align="center">1.05E-05</p></td><br />
<td width="69"><p align="center">3.88E-05</p></td><br />
<td width="69"><p align="center">5.23E-06</p></td><br />
<td width="69"><p align="center">2.44E205</p></td><br />
<td width="69"><p align="center">1.70E203</p></td><br />
<td width="69"><p align="center">3.7</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppD</em></p></td><br />
<td width="69"><p align="center">2.32E-05</p></td><br />
<td width="69"><p align="center">8.02E-05</p></td><br />
<td width="69"><p align="center">1.81E-05</p></td><br />
<td width="69"><p align="center">1.66E205</p></td><br />
<td width="69"><p align="center">3.50E203</p></td><br />
<td width="69"><p align="center">3.46</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>glnL</em></p></td><br />
<td width="69"><p align="center">2.41E-04</p></td><br />
<td width="69"><p align="center">3.99E-05</p></td><br />
<td width="69"><p align="center">4.81E-05</p></td><br />
<td width="69"><p align="center">2.81E205</p></td><br />
<td width="69"><p align="center">3.60E204</p></td><br />
<td width="69"><p align="center">-6.04</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppA</em></p></td><br />
<td width="69"><p align="center">2.54E-03</p></td><br />
<td width="69"><p align="center">5.06E-03</p></td><br />
<td width="69"><p align="center">1.72E-04</p></td><br />
<td width="69"><p align="center">5.68E204</p></td><br />
<td width="69"><p align="center">1.40E204</p></td><br />
<td width="69"><p align="center">2</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppB</em></p></td><br />
<td width="69"><p align="center">1.06E-04</p></td><br />
<td width="69"><p align="center">3.57E-04</p></td><br />
<td width="69"><p align="center">3.06E-05</p></td><br />
<td width="69"><p align="center">6.22E205</p></td><br />
<td width="69"><p align="center">3.60E204</p></td><br />
<td width="69"><p align="center">3.35</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>proV</em></p></td><br />
<td width="69"><p align="center">2.50E-05</p></td><br />
<td width="69"><p align="center">5.30E-05</p></td><br />
<td width="69"><p align="center">7.34E-06</p></td><br />
<td width="69"><p align="center">9.57E206<strong></strong></p></td><br />
<td width="69"><p align="center">3.60E203</p></td><br />
<td width="69"><p align="center">2.12</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>rbsC</em></p></td><br />
<td width="69"><p align="center">4.20E-05</p></td><br />
<td width="69"><p align="center">1.12E-04</p></td><br />
<td width="69"><p align="center">1.47E-05</p></td><br />
<td width="69"><p align="center">2.70E205</p></td><br />
<td width="69"><p align="center">3.90E203</p></td><br />
<td width="69"><p align="center">2.67</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>hdeB</em></p></td><br />
<td width="69"><p align="center">1.09E-03</p></td><br />
<td width="69"><p align="center">5.51E-06</p></td><br />
<td width="69"><p align="center">1.80E-04</p></td><br />
<td width="69"><p align="center">3.47E206</p></td><br />
<td width="69"><p align="center">2.00E205</p></td><br />
<td width="69"><p align="center">-198.5</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>yefM</em></p></td><br />
<td width="69"><p align="center">4.63E-04</p></td><br />
<td width="69"><p align="center">8.12E-04</p></td><br />
<td width="69"><p align="center">5.02E-05</p></td><br />
<td width="69"><p align="center">6.07E205</p></td><br />
<td width="69"><p align="center">1.10E204</p></td><br />
<td width="69"><p align="center">1.75</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
</table><br />
<br />
<br />
<p align="justify">The compare result means that whether the result of our software fit to the result of gene expression profile. After statistic, in these 30 genes, there are 21 genes whose result are same to gNAP’s simulation, 70% of the total.</p><br />
<br />
<br />
</br><br />
<h3 id="ggep">Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF LEUCINE-RESPONSIVE REGULATORY PROTEIN</h3><br />
<br />
<p align="justify">In this literature, researchers measure the gene expression profiles in Escherichia coli k12 with the effects of leucine-responsive regulatory protein(Lrp). And Lrp is one of the genes in our genetic regulatory network(GRN). </br></br><br />
By importing the Lrp’s promoter and gene sequence, we used our software simulating the enhancement of Lrp’s expression and compared the result with the gene expression profile in that literature.</br></br><br />
There are 22 genes in that profile which are also in our GRN. Here is the list and Genes differentially expressed between lrp+ and lrp- (control vs. experimental) E. coli strains with a p value less than 0.001:<br />
</p><br />
<br />
<table width="1000px" border="0" cellspacing="0" cellpadding="0" style="border-top:2px #000000 solid;border-bottom:2px #000000 solid;background: url(https://static.igem.org/mediawiki/2013/1/1e/M_1243691170517.jpg) repeat;"><br />
<tr><br />
<td width="64" style="border-bottom:2px #000 solid;"><p align="center">Gene name</p></td><br />
<td width="66" style="border-bottom:2px #000 solid;"><p align="center">Control</p></td><br />
<td width="64" style="border-bottom:2px #000 solid;"><p align="center">Experimental</p></td><br />
<td width="64" style="border-bottom:2px #000 solid;"><p align="center">Control</p></td><br />
<td width="64" style="border-bottom:2px #000 solid;"><p align="center">Experimental</p></td><br />
<td width="64" style="border-bottom:2px #000 solid;"><p align="center">p value</p></td><br />
<td width="76" style="border-bottom:2px #000 solid;"><p align="center">PPDE(&lt;p)</p></td><br />
<td width="52" style="border-bottom:2px #000 solid;"><p align="center">Fold</p></td><br />
<td width="64" style="border-bottom:2px #000 solid;"><p align="center">Compare result</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>&nbsp;</em></p></td><br />
<td width="66"><p align="center">mean</p></td><br />
<td width="64"><p align="center">mean</p></td><br />
<td width="64"><p align="center">S.D.</p></td><br />
<td width="64"><p align="center">S.D.</p></td><br />
<td width="64"><p align="center">&nbsp;</p></td><br />
<td width="76"><p align="center">&nbsp;</p></td><br />
<td width="52"><p align="center">&nbsp;</p></td><br />
<td width="64"><p align="center">&nbsp;</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>uvrA</em></p></td><br />
<td width="66"><p align="center">0.00128</p></td><br />
<td width="64"><p align="center">0.00104</p></td><br />
<td width="64"><p align="center">1.50E-05</p></td><br />
<td width="64"><p align="center">3.37E-05</p></td><br />
<td width="64"><p align="center">1.70E-05</p></td><br />
<td width="76"><p align="center">0.99386</p></td><br />
<td width="52"><p align="center">-1.23</p></td><br />
<td width="64"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>gdhA</em></p></td><br />
<td width="66"><p align="center">9.16E-05</p></td><br />
<td width="64"><p align="center">2.73E-04</p></td><br />
<td width="64"><p align="center">1.52E-05</p></td><br />
<td width="64"><p align="center">2.16E-05</p></td><br />
<td width="64"><p align="center">2.18E-05</p></td><br />
<td width="76"><p align="center">0.99329</p></td><br />
<td width="52"><p align="center">2.98</p></td><br />
<td width="64"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>oppB*</em></p></td><br />
<td width="66"><p align="center">7.51E-05</p></td><br />
<td width="64"><p align="center">0.00114</p></td><br />
<td width="64"><p align="center">2.12E-05</p></td><br />
<td width="64"><p align="center">3.79E-04</p></td><br />
<td width="64"><p align="center">2.48E-05</p></td><br />
<td width="76"><p align="center">0.99298</p></td><br />
<td width="52"><p align="center">15.12</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>artP</em></p></td><br />
<td width="66"><p align="center">6.73E-05</p></td><br />
<td width="64"><p align="center">4.23E-04</p></td><br />
<td width="64"><p align="center">1.24E-05</p></td><br />
<td width="64"><p align="center">1.16E-04</p></td><br />
<td width="64"><p align="center">3.60E-05</p></td><br />
<td width="76"><p align="center">0.992</p></td><br />
<td width="52"><p align="center">6.28</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>oppC*</em></p></td><br />
<td width="66"><p align="center">2.01E-04</p></td><br />
<td width="64"><p align="center">0.00108</p></td><br />
<td width="64"><p align="center">2.34E-05</p></td><br />
<td width="64"><p align="center">3.61E-04</p></td><br />
<td width="64"><p align="center">5.44E-05</p></td><br />
<td width="76"><p align="center">0.99074</p></td><br />
<td width="52"><p align="center">5.38</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>gltD*</em></p></td><br />
<td width="66"><p align="center">5.28E-04</p></td><br />
<td width="64"><p align="center">2.74E-05</p></td><br />
<td width="64"><p align="center">1.28E-04</p></td><br />
<td width="64"><p align="center">1.42E-05</p></td><br />
<td width="64"><p align="center">5.87E-05</p></td><br />
<td width="76"><p align="center">0.99049</p></td><br />
<td width="52"><p align="center">-19.27</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>oppA*</em></p></td><br />
<td width="66"><p align="center">0.00162</p></td><br />
<td width="64"><p align="center">0.0316</p></td><br />
<td width="64"><p align="center">7.63E-04</p></td><br />
<td width="64"><p align="center">0.0103</p></td><br />
<td width="64"><p align="center">8.45E-05</p></td><br />
<td width="76"><p align="center">0.9892</p></td><br />
<td width="52"><p align="center">19.44</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>malE*</em></p></td><br />
<td width="66"><p align="center">3.56E-04</p></td><br />
<td width="64"><p align="center">2.01E-04</p></td><br />
<td width="64"><p align="center">2.32E-05</p></td><br />
<td width="64"><p align="center">2.17E-05</p></td><br />
<td width="64"><p align="center">1.16E-04</p></td><br />
<td width="76"><p align="center">0.98793</p></td><br />
<td width="52"><p align="center">-1.78</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>oppD*</em></p></td><br />
<td width="66"><p align="center">8.97E-05</p></td><br />
<td width="64"><p align="center">6.55E-04</p></td><br />
<td width="64"><p align="center">2.76E-05</p></td><br />
<td width="64"><p align="center">2.05E-04</p></td><br />
<td width="64"><p align="center">1.16E-04</p></td><br />
<td width="76"><p align="center">0.98793</p></td><br />
<td width="52"><p align="center">7.3</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>galP</em></p></td><br />
<td width="66"><p align="center">3.75E-04</p></td><br />
<td width="64"><p align="center">2.11E-04</p></td><br />
<td width="64"><p align="center">2.25E-05</p></td><br />
<td width="64"><p align="center">2.40E-05</p></td><br />
<td width="64"><p align="center">1.31E-04</p></td><br />
<td width="76"><p align="center">0.9874</p></td><br />
<td width="52"><p align="center">-1.78</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>lysU*</em></p></td><br />
<td width="66"><p align="center">1.81E-04</p></td><br />
<td width="64"><p align="center">0.00124</p></td><br />
<td width="64"><p align="center">7.48E-05</p></td><br />
<td width="64"><p align="center">2.78E-04</p></td><br />
<td width="64"><p align="center">1.44E-04</p></td><br />
<td width="76"><p align="center">0.98697</p></td><br />
<td width="52"><p align="center">6.87</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>hybA</em></p></td><br />
<td width="66"><p align="center">3.53E-04</p></td><br />
<td width="64"><p align="center">2.47E-04</p></td><br />
<td width="64"><p align="center">2.11E-05</p></td><br />
<td width="64"><p align="center">1.50E-05</p></td><br />
<td width="64"><p align="center">1.49E-04</p></td><br />
<td width="76"><p align="center">0.98682</p></td><br />
<td width="52"><p align="center">-1.43</p></td><br />
<td width="64"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>hybC</em></p></td><br />
<td width="66"><p align="center">3.54E-04</p></td><br />
<td width="64"><p align="center">2.34E-04</p></td><br />
<td width="64"><p align="center">2.20E-05</p></td><br />
<td width="64"><p align="center">1.81E-05</p></td><br />
<td width="64"><p align="center">1.61E-04</p></td><br />
<td width="76"><p align="center">0.98646</p></td><br />
<td width="52"><p align="center">-1.51</p></td><br />
<td width="64"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>ilvG_1*</em></p></td><br />
<td width="66"><p align="center">4.21E-04</p></td><br />
<td width="64"><p align="center">9.15E-04</p></td><br />
<td width="64"><p align="center">7.55E-05</p></td><br />
<td width="64"><p align="center">6.85E-05</p></td><br />
<td width="64"><p align="center">2.54E-04</p></td><br />
<td width="76"><p align="center">0.98411</p></td><br />
<td width="52"><p align="center">2.17</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>phoP</em></p></td><br />
<td width="66"><p align="center">8.29E-05</p></td><br />
<td width="64"><p align="center">2.10E-04</p></td><br />
<td width="64"><p align="center">1.20E-05</p></td><br />
<td width="64"><p align="center">4.42E-05</p></td><br />
<td width="64"><p align="center">3.16E-04</p></td><br />
<td width="76"><p align="center">0.98285</p></td><br />
<td width="52"><p align="center">2.54</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>emrA</em></p></td><br />
<td width="66"><p align="center">3.58E-04</p></td><br />
<td width="64"><p align="center">2.78E-04</p></td><br />
<td width="64"><p align="center">2.43E-05</p></td><br />
<td width="64"><p align="center">4.57E-06</p></td><br />
<td width="64"><p align="center">3.95E-04</p></td><br />
<td width="76"><p align="center">0.98147</p></td><br />
<td width="52"><p align="center">-1.29</p></td><br />
<td width="64"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>glpA</em></p></td><br />
<td width="66"><p align="center">1.28E-04</p></td><br />
<td width="64"><p align="center">8.01E-05</p></td><br />
<td width="64"><p align="center">8.54E-06</p></td><br />
<td width="64"><p align="center">9.26E-06</p></td><br />
<td width="64"><p align="center">4.71E-04</p></td><br />
<td width="76"><p align="center">0.98029</p></td><br />
<td width="52"><p align="center">-1.59</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>manA</em></p></td><br />
<td width="66"><p align="center">8.71E-05</p></td><br />
<td width="64"><p align="center">2.40E-04</p></td><br />
<td width="64"><p align="center">2.16E-05</p></td><br />
<td width="64"><p align="center">4.08E-05</p></td><br />
<td width="64"><p align="center">4.80E-04</p></td><br />
<td width="76"><p align="center">0.98016</p></td><br />
<td width="52"><p align="center">2.75</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>amn</em></p></td><br />
<td width="66"><p align="center">4.31E-04</p></td><br />
<td width="64"><p align="center">6.51E-04</p></td><br />
<td width="64"><p align="center">4.47E-05</p></td><br />
<td width="64"><p align="center">4.72E-05</p></td><br />
<td width="64"><p align="center">6.07E-04</p></td><br />
<td width="76"><p align="center">0.97848</p></td><br />
<td width="52"><p align="center">1.51</p></td><br />
<td width="64"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>speB</em></p></td><br />
<td width="66"><p align="center">1.21E-04</p></td><br />
<td width="64"><p align="center">3.56E-05</p></td><br />
<td width="64"><p align="center">2.09E-05</p></td><br />
<td width="64"><p align="center">1.08E-05</p></td><br />
<td width="64"><p align="center">7.73E-04</p></td><br />
<td width="76"><p align="center">0.97659</p></td><br />
<td width="52"><p align="center">-3.4</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>hdeA</em></p></td><br />
<td width="66"><p align="center">2.40E-04</p></td><br />
<td width="64"><p align="center">8.29E-04</p></td><br />
<td width="64"><p align="center">8.46E-05</p></td><br />
<td width="64"><p align="center">9.90E-05</p></td><br />
<td width="64"><p align="center">8.12E-04</p></td><br />
<td width="76"><p align="center">0.97619</p></td><br />
<td width="52"><p align="center">3.45</p></td><br />
<td width="64"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="64"><p align="center"><em>lrp*</em></p></td><br />
<td width="66"><p align="center">2.96E-04</p></td><br />
<td width="64"><p align="center">1.11E-04</p></td><br />
<td width="64"><p align="center">6.21E-05</p></td><br />
<td width="64"><p align="center">2.22E-05</p></td><br />
<td width="64"><p align="center">8.27E-04</p></td><br />
<td width="76"><p align="center">0.97604</p></td><br />
<td width="52"><p align="center">-2.67</p></td><br />
<td width="64"><p align="center">unfit</p></td><br />
</tr><br />
</table><br />
<br />
<p align="justify">The compare result means that whether the result of our software fit to the result of gene expression profile. After statistic, in these 22 genes, there are 15 genes whose result are same to gNAP’s simulation, 68.2% of the total.</p><br />
<br />
<br />
</br><br />
<h3 id="ggepe">Global Gene Expression Profiling in Escherichia coli K12 THE EFFECTS OF OXYGEN AVAILABILITY AND FNR</h3><br />
<br />
<p align="justify">In this literature, researchers measure the gene expression profiles in Escherichia coli k12 with the effects of oxygen availability and FNR. And FNR is one of the genes in our genetic regulatory network(GRN). We do not consider the effect of oxygen, but instead, we control the oxygen in the same way and consider the effect of FNR+ and FNR-.</br></br><br />
By importing the FNR’s promoter and gene sequence, we used our software simulating the enhancement of FNR’s expression and compared the result with the gene expression profile in that literature.</br></br><br />
There are 38 genes in that profile which are also in our GRN. Here is the list and Genes differentially expressed between FNR+ and FNR- E. coli strains:<br />
</p><br />
<br />
<br />
<table width="1000px" border="0" cellspacing="0" cellpadding="0" style="border-top:2px #000000 solid;border-bottom:2px #000000 solid;background: url(https://static.igem.org/mediawiki/2013/1/1e/M_1243691170517.jpg) repeat;"><br />
<tr><br />
<td width="114" style="border-bottom:2px #000000 solid;"><p align="center">Gene</p></td><br />
<td width="114" style="border-bottom:2px #000000 solid;"><p align="center">p value</p></td><br />
<td width="114" style="border-bottom:2px #000000 solid;"><p align="center">PPDE(&lt;p)</p></td><br />
<td width="114" style="border-bottom:2px #000000 solid;"><p align="center">Fold</p></td><br />
<td width="114" style="border-bottom:2px #000000 solid;"><p align="center">Compare result</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>trpB</em></p></td><br />
<td width="114"><p align="center">4.94E-04</p></td><br />
<td width="114"><p align="center">0.99872</p></td><br />
<td width="114"><p align="center">9.24</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>cyoA</em></p></td><br />
<td width="114"><p align="center">6.96E-07</p></td><br />
<td width="114"><p align="center">0.99999</p></td><br />
<td width="114"><p align="center">13.05</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>gpmA</em></p></td><br />
<td width="114"><p align="center">1.73E-04</p></td><br />
<td width="114"><p align="center">0.99939</p></td><br />
<td width="114"><p align="center">10.41</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>crr</em></p></td><br />
<td width="114"><p align="center">6.26E-05</p></td><br />
<td width="114"><p align="center">0.9997</p></td><br />
<td width="114"><p align="center">3.47</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>nuoE</em></p></td><br />
<td width="114"><p align="center">1.17E-04</p></td><br />
<td width="114"><p align="center">0.99953</p></td><br />
<td width="114"><p align="center">4.49</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>rplM</em></p></td><br />
<td width="114"><p align="center">5.72E-06</p></td><br />
<td width="114"><p align="center">0.99994</p></td><br />
<td width="114"><p align="center">14.32</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>gatY</em></p></td><br />
<td width="114"><p align="center">1.92E-04</p></td><br />
<td width="114"><p align="center">0.99934</p></td><br />
<td width="114"><p align="center">8</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>trmD</em></p></td><br />
<td width="114"><p align="center">1.64E-04</p></td><br />
<td width="114"><p align="center">0.99941</p></td><br />
<td width="114"><p align="center">2.5</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>ndh</em></p></td><br />
<td width="114"><p align="center">8.73E-06</p></td><br />
<td width="114"><p align="center">0.99992</p></td><br />
<td width="114"><p align="center">5.06</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>manY</em></p></td><br />
<td width="114"><p align="center">2.49E-04</p></td><br />
<td width="114"><p align="center">0.99921</p></td><br />
<td width="114"><p align="center">8.35</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>manZ</em></p></td><br />
<td width="114"><p align="center">1.07E-04</p></td><br />
<td width="114"><p align="center">0.99956</p></td><br />
<td width="114"><p align="center">2.53</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>ompA</em></p></td><br />
<td width="114"><p align="center">6.57E-06</p></td><br />
<td width="114"><p align="center">0.99994</p></td><br />
<td width="114"><p align="center">3.41</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>rplT</em></p></td><br />
<td width="114"><p align="center">3.68E-05</p></td><br />
<td width="114"><p align="center">0.99979</p></td><br />
<td width="114"><p align="center">8.41</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>rpsJ</em></p></td><br />
<td width="114"><p align="center">6.23E-04</p></td><br />
<td width="114"><p align="center">0.99849</p></td><br />
<td width="114"><p align="center">4.27</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>cydA</em></p></td><br />
<td width="114"><p align="center">3.15E-04</p></td><br />
<td width="114"><p align="center">0.99906</p></td><br />
<td width="114"><p align="center">4.73</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>rplS</em></p></td><br />
<td width="114"><p align="center">2.70E-07</p></td><br />
<td width="114"><p align="center">0.99999</p></td><br />
<td width="114"><p align="center">6.24</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>ptsG</em></p></td><br />
<td width="114"><p align="center">3.41E-04</p></td><br />
<td width="114"><p align="center">0.99901</p></td><br />
<td width="114"><p align="center">3.17</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>oppA</em></p></td><br />
<td width="114"><p align="center">9.06E-14</p></td><br />
<td width="114"><p align="center">1</p></td><br />
<td width="114"><p align="center">4.53</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>talA</em></p></td><br />
<td width="114"><p align="center">7.62E-05</p></td><br />
<td width="114"><p align="center">0.99965</p></td><br />
<td width="114"><p align="center">2.76</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>fdhF</em></p></td><br />
<td width="114"><p align="center">1.84E-04</p></td><br />
<td width="114"><p align="center">0.99936</p></td><br />
<td width="114"><p align="center">-2.28</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>caiT</em></p></td><br />
<td width="114"><p align="center">2.28E-07</p></td><br />
<td width="114"><p align="center">0.99999</p></td><br />
<td width="114"><p align="center">-6.62</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>pyrD</em></p></td><br />
<td width="114"><p align="center">6.25E-06</p></td><br />
<td width="114"><p align="center">0.99994</p></td><br />
<td width="114"><p align="center">-13.74</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>recC</em></p></td><br />
<td width="114"><p align="center">6.38E-05</p></td><br />
<td width="114"><p align="center">0.99969</p></td><br />
<td width="114"><p align="center">-2.29</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>tdh</em></p></td><br />
<td width="114"><p align="center">1.06E-05</p></td><br />
<td width="114"><p align="center">0.99991</p></td><br />
<td width="114"><p align="center">-3.01</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>araB</em></p></td><br />
<td width="114"><p align="center">1.21E-04</p></td><br />
<td width="114"><p align="center">0.99951</p></td><br />
<td width="114"><p align="center">-3.59</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>nanT</em></p></td><br />
<td width="114"><p align="center">1.13E-05</p></td><br />
<td width="114"><p align="center">0.99991</p></td><br />
<td width="114"><p align="center">-3.04</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>acrF</em></p></td><br />
<td width="114"><p align="center">1.53E-06</p></td><br />
<td width="114"><p align="center">0.99998</p></td><br />
<td width="114"><p align="center">-6.83</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>pstS</em></p></td><br />
<td width="114"><p align="center">4.63E-05</p></td><br />
<td width="114"><p align="center">0.99975</p></td><br />
<td width="114"><p align="center">-4.35</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>metL</em></p></td><br />
<td width="114"><p align="center">3.16E-06</p></td><br />
<td width="114"><p align="center">0.99996</p></td><br />
<td width="114"><p align="center">-5.65</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>mhpF</em></p></td><br />
<td width="114"><p align="center">2.77E-05</p></td><br />
<td width="114"><p align="center">0.99983</p></td><br />
<td width="114"><p align="center">-5.92</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>glgA</em></p></td><br />
<td width="114"><p align="center">3.51E-04</p></td><br />
<td width="114"><p align="center">0.99899</p></td><br />
<td width="114"><p align="center">-2.33</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>glnD</em></p></td><br />
<td width="114"><p align="center">1.09E-05</p></td><br />
<td width="114"><p align="center">0.99991</p></td><br />
<td width="114"><p align="center">-6.32</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>uraA</em></p></td><br />
<td width="114"><p align="center">2.11E-04</p></td><br />
<td width="114"><p align="center">0.99929</p></td><br />
<td width="114"><p align="center">-2.48</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>speC</em></p></td><br />
<td width="114"><p align="center">2.49E-06</p></td><br />
<td width="114"><p align="center">0.99997</p></td><br />
<td width="114"><p align="center">-3.54</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>fliP</em></p></td><br />
<td width="114"><p align="center">6.45E-04</p></td><br />
<td width="114"><p align="center">0.99846</p></td><br />
<td width="114"><p align="center">-3.58</p></td><br />
<td width="114"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>dinG</em></p></td><br />
<td width="114"><p align="center">4.66E-05</p></td><br />
<td width="114"><p align="center">0.99975</p></td><br />
<td width="114"><p align="center">-3.14</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>proW</em></p></td><br />
<td width="114"><p align="center">3.73E-06</p></td><br />
<td width="114"><p align="center">0.99996</p></td><br />
<td width="114"><p align="center">-4.97</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="114"><p align="center"><em>sbcC</em></p></td><br />
<td width="114"><p align="center">5.66E-04</p></td><br />
<td width="114"><p align="center">0.99859</p></td><br />
<td width="114"><p align="center">-3.06</p></td><br />
<td width="114"><p align="center">unfit</p></td><br />
</tr><br />
</table><br />
<br />
<p align="justify">The compare result means that whether the result of our software fit to the result of gene expression profile. After statistic, in these 38 genes, there are 25 genes whose result are same to gNAP’s simulation, 65.8% of the total.</p><br />
<br />
<br />
</br><br />
<h3 id="ggepec">Global Gene Expression Profiling in Escherichia coli K12 EFFECTS OF OXYGEN AVAILABILITY AND ArcA</h3><br />
<br />
<p align="justify">In this literature, researchers measure the gene expression profiles in Escherichia coli k12 with the effects of oxygen availability and arcA. And arcA is one of the genes in our genetic regulatory network(GRN). We do not consider the effect of oxygen, but instead, we control the oxygen in the same way and consider the effect of arcA+ and arcA-.</br></br><br />
By importing the arcA’s promoter and gene sequence, we used our software simulating the enhancement of arcA’s expression and compared the result with the gene expression profile in that literature.</br></br><br />
There are 43 genes in that profile which are also in our arcA. Here is the list and Genes differentially expressed between arcA+ and arcA- E. coli strains:<br />
</p><br />
<br />
<table width="1000px" border="0" cellspacing="0" cellpadding="0" style="border-top:2px #000000 solid;border-bottom:2px #000000 solid;background: url(https://static.igem.org/mediawiki/2013/1/1e/M_1243691170517.jpg) repeat;"><br />
<tr><br />
<td width="187" style="border-bottom:2px #000000 solid;"><p align="center">Gene name(NIH) and b no.</p></td><br />
<td width="102" style="border-bottom:2px #000000 solid;"><p align="center">p value</p></td><br />
<td width="101" style="border-bottom:2px #000000 solid;"><p align="center">PPDE(&lt;p)</p></td><br />
<td width="67" style="border-bottom:2px #000000 solid;"><p align="center">-Fold</p></td><br />
<td width="115" style="border-bottom:2px #000000 solid;"><p align="center">Compare result</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">talA(b2464)</p></td><br />
<td width="102"><p align="center">2.21E-04</p></td><br />
<td width="101"><p align="center">0.99933</p></td><br />
<td width="67"><p align="center">3.46</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">crr(b2417)</p></td><br />
<td width="102"><p align="center">2.14E-09</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">3.63</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">oppA(b1243)</p></td><br />
<td width="102"><p align="center">8.78E-05</p></td><br />
<td width="101"><p align="center">0.99966</p></td><br />
<td width="67"><p align="center">3.79</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">rpsJ(b3321)</p></td><br />
<td width="102"><p align="center">6.28E-06</p></td><br />
<td width="101"><p align="center">0.99995</p></td><br />
<td width="67"><p align="center">3.8</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">ompA(b0957)</p></td><br />
<td width="102"><p align="center">1.48E-06</p></td><br />
<td width="101"><p align="center">0.99998</p></td><br />
<td width="67"><p align="center">4.02</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">rbsD(b3748)</p></td><br />
<td width="102"><p align="center">3.85E-08</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">4.83</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">rplS(b2606)</p></td><br />
<td width="102"><p align="center">3.02E-05</p></td><br />
<td width="101"><p align="center">0.99984</p></td><br />
<td width="67"><p align="center">5.81</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">nuoE(b2285)</p></td><br />
<td width="102"><p align="center">2.34E-09</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">8.83</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">rplT(b1716)</p></td><br />
<td width="102"><p align="center">4.41E-06</p></td><br />
<td width="101"><p align="center">0.99996</p></td><br />
<td width="67"><p align="center">9.01</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">gatY(b2096)</p></td><br />
<td width="102"><p align="center">3.49E-07</p></td><br />
<td width="101"><p align="center">0.99999</p></td><br />
<td width="67"><p align="center">10.72</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">sdhA(b0723)</p></td><br />
<td width="102"><p align="center">2.06E-07</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">14.54</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">gpmA(b0755)</p></td><br />
<td width="102"><p align="center">1.27E-09</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">16.95</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">rplM(b3231)</p></td><br />
<td width="102"><p align="center">3.40E-07</p></td><br />
<td width="101"><p align="center">0.99999</p></td><br />
<td width="67"><p align="center">17.05</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">mdh(b3236)</p></td><br />
<td width="102"><p align="center">4.00E-04</p></td><br />
<td width="101"><p align="center">0.99896</p></td><br />
<td width="67"><p align="center">17.95</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">nuoB(b2287)</p></td><br />
<td width="102"><p align="center">1.32E-04</p></td><br />
<td width="101"><p align="center">0.99954</p></td><br />
<td width="67"><p align="center">19.34</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">trpB(b1261)</p></td><br />
<td width="102"><p align="center">3.12E-10</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">19.97</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">cyoA(b0432)</p></td><br />
<td width="102"><p align="center">9.70E-10</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">23.3</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">sdhB(b0724)</p></td><br />
<td width="102"><p align="center">1.25E-05</p></td><br />
<td width="101"><p align="center">0.99992</p></td><br />
<td width="67"><p align="center">27.87</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">sucD(b0729)</p></td><br />
<td width="102"><p align="center">2.42E-05</p></td><br />
<td width="101"><p align="center">0.99987</p></td><br />
<td width="67"><p align="center">86.14</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">gltA(b0720)</p></td><br />
<td width="102"><p align="center">3.09E-05</p></td><br />
<td width="101"><p align="center">0.99984</p></td><br />
<td width="67"><p align="center">107.01</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">pyrD(b0945)</p></td><br />
<td width="102"><p align="center">5.36E-06</p></td><br />
<td width="101"><p align="center">0.99996</p></td><br />
<td width="67"><p align="center">-18.91</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">dinG(b0799)</p></td><br />
<td width="102"><p align="center">3.20E-04</p></td><br />
<td width="101"><p align="center">0.99912</p></td><br />
<td width="67"><p align="center">-11.79</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">gadB(b1493)</p></td><br />
<td width="102"><p align="center">1.87E-08</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">-11.23</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">gadA(b3517)</p></td><br />
<td width="102"><p align="center">5.14E-07</p></td><br />
<td width="101"><p align="center">0.99999</p></td><br />
<td width="67"><p align="center">-9.44</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">glnD(b0167)</p></td><br />
<td width="102"><p align="center">8.77E-05</p></td><br />
<td width="101"><p align="center">0.99966</p></td><br />
<td width="67"><p align="center">-8.58</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">aroM(b0390)</p></td><br />
<td width="102"><p align="center">1.81E-04</p></td><br />
<td width="101"><p align="center">0.99942</p></td><br />
<td width="67"><p align="center">-7.13</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">pnuC(b0751)</p></td><br />
<td width="102"><p align="center">1.71E-05</p></td><br />
<td width="101"><p align="center">0.9999</p></td><br />
<td width="67"><p align="center">-6.22</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">gadX(b3516)</p></td><br />
<td width="102"><p align="center">2.32E-06</p></td><br />
<td width="101"><p align="center">0.99998</p></td><br />
<td width="67"><p align="center">-6.11</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">sbcC(b0397)</p></td><br />
<td width="102"><p align="center">3.89E-04</p></td><br />
<td width="101"><p align="center">0.99898</p></td><br />
<td width="67"><p align="center">-5.59</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">xylR(b3569)</p></td><br />
<td width="102"><p align="center">3.55E-04</p></td><br />
<td width="101"><p align="center">0.99905</p></td><br />
<td width="67"><p align="center">-5.11</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">gadW(b3515)</p></td><br />
<td width="102"><p align="center">2.41E-05</p></td><br />
<td width="101"><p align="center">0.99987</p></td><br />
<td width="67"><p align="center">-4.63</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">recC(b2822)</p></td><br />
<td width="102"><p align="center">2.98E-05</p></td><br />
<td width="101"><p align="center">0.99985</p></td><br />
<td width="67"><p align="center">-4.62</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">appC(b0978)</p></td><br />
<td width="102"><p align="center">4.83E-09</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">-4.01</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">speC(b2965)</p></td><br />
<td width="102"><p align="center">2.86E-04</p></td><br />
<td width="101"><p align="center">0.99919</p></td><br />
<td width="67"><p align="center">-2.97</p></td><br />
<td width="115"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">glgA(b3429)</p></td><br />
<td width="102"><p align="center">1.74E-04</p></td><br />
<td width="101"><p align="center">0.99944</p></td><br />
<td width="67"><p align="center">-2.9</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">nanT(b3224)</p></td><br />
<td width="102"><p align="center">7.22E-05</p></td><br />
<td width="101"><p align="center">0.99971</p></td><br />
<td width="67"><p align="center">-2.46</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">appB(b0979)</p></td><br />
<td width="102"><p align="center">3.31E-09</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">-2.43</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">rhaA(b3903)</p></td><br />
<td width="102"><p align="center">1.48E-04</p></td><br />
<td width="101"><p align="center">0.9995</p></td><br />
<td width="67"><p align="center">-2.41</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">hycD(b2722)</p></td><br />
<td width="102"><p align="center">1.50E-05</p></td><br />
<td width="101"><p align="center">0.99991</p></td><br />
<td width="67"><p align="center">-2.21</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">hdeA(b3510)</p></td><br />
<td width="102"><p align="center">4.82E-09</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">-2.83</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">hyaB(b0973)</p></td><br />
<td width="102"><p align="center">7.30E-08</p></td><br />
<td width="101"><p align="center">1</p></td><br />
<td width="67"><p align="center">-2.83</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">uraA(b2497)</p></td><br />
<td width="102"><p align="center">7.91E-05</p></td><br />
<td width="101"><p align="center">0.99968</p></td><br />
<td width="67"><p align="center">-2.6</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="187"><p align="center">glgC(b3430)</p></td><br />
<td width="102"><p align="center">4.69E-04</p></td><br />
<td width="101"><p align="center">0.99883</p></td><br />
<td width="67"><p align="center">-2.15</p></td><br />
<td width="115"><p align="center">fit</p></td><br />
</tr><br />
</table><br />
<br />
<p align="justify">The compare result means that whether the result of our software fit to the result of gene expression profile. After statistic, in these 38 genes, there are 25 genes whose result are same to gNAP’s simulation, 62.8% of the total.</p><br />
<br />
<br />
<br />
<h2 id="consistency">Consistency</h2><br />
<p>The consistency of the program has also been tested. We inserted a gene as same as a<br />
gene in the network and compared the regulations predicted by the program with the<br />
original regulations. Without filtering the random similarities, the actual regulations were<br />
submerged in the network noise.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/2/2a/USTC_Software_Theoretical_Regulation_wiki.png" /><br />
<p><strong>Figure 1.</strong> The green line represents regulating values.<br /><br />
The blue line<br />
represents regulated values.</p></div><br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/5/59/USTC_Software_Predicted_Regulation_wiki.png" /><br />
<p><strong>Figure 2.</strong>Predicted regulation without filtered.<br /><br />
The actual regulations are submerged by the<br />
noise.</p></div><br />
<br />
<p>With random similarities filtered, all original regulations were picked out. The result shows<br />
that the program is consistent with the original network.</p><br />
<br /><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/b/b9/USTC_Software_Predicted_Regulation_%28Filtered%29.png" /><br />
<p><strong>Figure 3.</strong>The SNR is better.<br /><br />
The actual regulations are<br />
picked out.</p></div><br />
<br />
<br />
<h2 id="sum1">Summary</h2><br />
<br />
<p align="justify">In first two literatures, without the limit of oxygen, the average fitness is up to 69.1%. And in the other two literatures, the average fitness is 64.3%. We thought that it may be the oxygen’s limit which affect the expression of each gene. Gene regulatory network analysis has its weakness about environment’s change.</br></br><br />
All in all, the total average of fitness is still up to 66.7%. Therefore, we may draw the following conclusion that our software could simulate the impact of new gene to some extent.<br />
</p><br />
<br />
<br />
<h2 id="reference">Reference</h2><br />
<p align="justify"><a class="content" href="http://www.jbc.org/content/275/38/29672.short">Arfin S M, Long A D, Ito E T, et al. Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF INTEGRATION HOST FACTOR[J]. Journal of Biological Chemistry, 2000, 275(38): 29672-29684.</a></br></br><br />
<br />
<a class="content" href="http://www.jbc.org/content/277/43/40309.short">Hung S, Baldi P, Hatfield G W. Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF LEUCINE-RESPONSIVE REGULATORY PROTEIN[J]. Journal of Biological Chemistry, 2002, 277(43): 40309-40323.</a></br></br><br />
<br />
<a class="content" href="http://www.jbc.org/content/278/32/29837.short">Salmon K, Hung S, Mekjian K, et al. Global Gene Expression Profiling in Escherichia coli K12 THE EFFECTS OF OXYGEN AVAILABILITY AND FNR[J]. Journal of Biological Chemistry, 2003, 278(32): 29837-29855.</a></br></br><br />
<br />
<a class="content" href="http://www.jbc.org/content/280/15/15084.short">Salmon K A, Hung S, Steffen N R, et al. Global Gene Expression Profiling in Escherichia coli K12 EFFECTS OF OXYGEN AVAILABILITY AND ArcA[J]. Journal of Biological Chemistry, 2005, 280(15): 15084-15096.</a><br />
</p><br />
<br />
</div><br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/teamTeam:USTC-Software/team2013-10-27T16:00:37Z<p>USTCkun: </p>
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<div class="words"><br />
<p><br />
<b>Name</b>: Chenkun Wang(王晨坤)<br /><br />
<b>Age</b>: 21 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br />
<b>Major</b>: Condensed Matter Physics<br /><br />
<b>Grade</b>: Senior Undergraduate<br /><br />
<b>Division of labor</b>: Basic construction of software, software coding, GUI<br /><br />
<b>Email</b>: ustckun@gamil.com<br /><br />
<b>Brief Intro:</b><br />
Chenkun Wang is a senior undergraduate majoring in condensed matter physics. As a team leader of USTC-Software, he participated in nearly every part of project. He not only accomplished the basic construction of software, but also was responsible for the development of starter module as well as the database fetching coding. What's more, he completed the GUI of our software with the help of Shuai Ma.<br />
After nearly half year's working for the competition of iGEM_2013_Software, he became overwhelming interested in command line and simulation. He will graduate next year and he hopes to continue such work in his future studies. His dream is to pursue a Ph.D. in Electrical Engineering.</p><br />
</div><br />
</div><br />
<br />
<div id="yang" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/d/df/USTC-Software_yang.png" /></div><br />
<div class="words"><br />
<p><br />
<b>Name</b>: Jinyang Li (李进阳)<br /><br />
<b>Age</b>: 21 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br />
<b>Major</b>: Theoretical Physics<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Division of labor</b>: Software coding, design<br /><br />
<b>Email</b>: jinyangustc@gmail.com<br /><br />
<b>Brief Intro:</b><br />
Jinyang Li is a junior undergraduate majoring in Theoretical Physics. He is responsible for the development of network analysis module. He has reviewed some key literatures to provide theoretical support for the project. He also designed the prediction algorithm together with Chenkun Wang and improved it. In the development process, Jinyang has found an interesting phenomenon in the gene regulation network of Escherichia coli and prepared to study it further. He also made contribution to art design of the project.<br /><br />
At school, he is a director of a volunteer organization. Because of the work of the iGEM competition, he got interested in complex network and prepared to join the Nonlinear Science and Complex System Center(NLSC) of USTC.</p><br />
</div><br />
</div><br />
<br />
<div id="pei" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/c/cd/USTC-Software_Pei.png" /></div><br />
<div class="words"><br />
<p><br />
<b>Name</b>:Pei Zhang (张沛)<br /><br />
<b>Age</b>:19<br /><br />
<b>Major</b>:Computer Sciences and Technology<br /><br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:team manager<br /><br />
<b>Email</b>: zp33@mail.ustc.edu.cn<br /><br />
<b>Breif Intro:</b><br /><br />
Pei Roc Zhang is a sophomore undergraduate from the School of Computer Science and Technology. When joining the iGem_2013_Software_Team, he was a freshman undergraduate from the School of Life Sciences who loved CS. As a member of the team, he participates in software development and modifies English documents. What's more, as the manager of the team, he is in charge of all the fincial work and other small chores.<br /> <br />
As a sophomore who has just transfered to CS department, he is now struggling for a better academic performance and seeking for offers from top research facilities in the world in the near two years. </p><br />
</div><br />
</div><br />
<br />
<div id="yu" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/e/e2/Yu.png" /></div><br />
<div class="words"><br />
<p><br />
<b>Name</b>: Xingyu Liao (廖星宇)<br /><br />
<b>Age</b>: 19<br /><br />
<b>Major</b>: Theoretical Physics<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Email</b>: liaoxyn@gmail.com<br /><br />
<b>Brief Info :</b><br /><br />
He was in Department of Earth and Space Sciences when he was a freshman. He transferred to Department of Physics after the first academic year. He joined the USTC_software team out of interests in CS and was in charge of genetic network calculation division. The experience of working in igem team is really exciting, for he met a lot of people here and they worked as a team. He also learnt something new by himself and had a new vision on biology.<br />
</p><br />
<br />
</div><br />
</div><br />
<br />
<div id="hua" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/c/cd/USTC-Software_Shaohua.png" /></div><br />
<div class="words"><br />
<p><br />
<b>Name</b>: Shaohua Chen (陈少华)<br /><br />
<b>Major</b>:Nuclear Science and technology <br /><br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:wiki<br /><br />
<b>Email</b>: csh66@mail.ustc.edu.cn<br /><br />
<b>Brief Intro:</b><br /><br />
Hello,I am Shaohua Chen ,a sophomore undergraduate in USTC. Before joining in 2013 USTC-Software ,I knew little about the Internet.To change this bad situation ,I was determined to join in USTC-Software where I could not only learn Internet but also know more biology.So it's an honour for me to cooperate with many gifted and responsible teammates ,from whom I learned a lot. In the team , I am in charge of the wiki,during the process ,I find that a person's ability is limited,teamwork makes our work more efficient. <br />
</p><br />
<br />
</div><br />
</div><br />
<br />
<div id="jing" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/6/6a/USTC-Software_Jing.png"/></div><br />
<div class="words"><br />
<p><br />
<b>Name</b>:Jinjing Yi (易津晶)<br /><br />
<b>Age</b>:20<br /><br />
<b>Major</b>:Theoretical Physics<br /><br />
<b>Grade</b>:Junior Undergraduate<br /><br />
<b>Division of labor</b>:PSO, design, diary, Human Practice<br /><br />
<b>Email</b>: 1195036879@qq.com<br /><br />
Jinjing Yi is a junior undergraduate majoring in Theoretical Physics. She participated in the prediction part and finished the first version of partical swarm opimization algorithm. She is also responsible for diary writing and Human Practice.During this summer, she became interested in photoshop and helped design the team logo. Joining in this happy team made her more confident and more energetic.<br /><br />
She used to be a member in student union and meanwhile focused on her academic learning. In the last two years, she decided to join in the research in theory of condensed matter physics.<br />
</p><br />
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</div><br />
<br />
<div id="sen" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/5/5e/USTC-Software_Haosen.png" /></div><br />
<div class="words"><br />
<p><br />
<b>Name</b>:Haosen Tan (谈浩森)<br /><br />
<b>Age</b>:20 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br />
<b>Grade</b>: Sophomore undergraduate<br /><br />
<b>Major</b>: Physics <br /> <br />
<b>Division of Labor:</b><br />
Software coding, debugging and releasing the software, tutorial<br /><br />
<b>Email</b>: ths@mail.ustc.edu.cn<br /><br />
<b>Brief Intro</b>: <br />
A student from school of physics with much debug experience and debug skill. when joining the team, he was a freshman undergraduate. <br />
As a member of the team, he participates in software development and writes some English documents, for example, the tutorial and Quikstart. What's more, as the one of programmers of the team, he is in charge of debugging and releasing the software on different platform such as Windows, Mac OS X, Linux.<br /> <br />
He is now struggling for a better academic platform to learn something and share something. In addition, he firmly believes USTC-Software will do a good job in the Asia jamboree and World jamboree!<br />
</p><br />
</div><br />
</div><br />
<br />
<div id="shuai" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/6/6e/USTC-Software_Shuai.png" /></div><br />
<div class="words"><br />
<p><br />
<b>Name</b> : Shuai Ma (马帅)<br /><br />
<b>Major</b> : Nuclear Science and Technology<br /><br />
<b>Grade</b> : Junior Undergraduate<br /><br />
<b>Division of labor </b>: GUI<br /><br />
<b>Email</b>: ms369@mail.ustc.edu.cn<br /><br />
<b>Breif Intro :</b><br /><br />
Shuai Ma is a junior undergraduate from the School of Nuclear Science and Technology. In the team he is in charge of designing GUI with Qt . With little knowledge in C++, the job was a real challenge for him, yet he accomplished it with great determination and dedication. In everyday life, he enjoys all kinds of ball games while soccer ball is his favorite. <br />
<br />
</p><br />
</div><br />
</div><br />
<br />
<div id="nan" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/0/03/USTC-Software_Nan.png" /></div><br />
<div class="words"><br />
<p><br />
<b>Name</b>:Monan Zhou (周墨南)<br /><br />
<b>Age</b>:20<br /><br />
<b>Major</b>:Mathematical Sciences<br /> <br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:Wiki<br /><br />
<b>Email</b>: zmn2012@mail.ustc.edu.cn<br /><br />
<b>Brief Intro</b>:<br /><br />
Monan Zhou is a sophomore undergraduate from the School of Mathematical Sciences. When joining the 2013 USTC-Software, he was a freshman undergraduate from the School of Life Sciences who loved Math. As a member of the team, he participates in wiki.<br />
As a sophomore who has just transferred to School of Mathematical Sciences, he is now struggling for a better academic performance and seeking for offers from top research facilities in the world in the near two years. <br />
<br />
</p><br />
</div><br />
</div><br />
<br />
<div id="xhao" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/9/98/USTC-Software_hao.png" /></div><br />
<div class="words"><br />
<p><br />
<b>Name</b>:Hao Xu (徐浩)<br /><br />
<b>Age</b>:19<br /><br />
<b>Major</b>:Phisical Sciences<br /><br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:Data visualization<br /><br />
<b>Email</b>: xuhao1@163.com<br /><br />
<b>Breif Intro:</b><br /><br />
Hao Xu is always interested in lots of things,such as programming,airplane models and so on.Hao was in charge of the visualizition of data with java.In USTC-Software,he find that biology is even insteresting and he learned a lot.By the way ,He is too lazy to write a self intro...<a href="http://www.stlover.org">learn more</a><br />
</p><br />
</div><br />
</div><br />
<br />
<div id="hao" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/b/b2/USTC-SOftware-Hao.png" /></div><br />
<div class="words"><br />
<p><br />
<b>Name</b>: Tianhao Zhang (张天昊)<br /><br />
<b>Age</b>: 20<br /><br />
<b>Major</b>: Biophysics<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Division of labor</b>: Clotho App<br /><br />
<b>Email</b>: zhth1993@gmail.com<br /><br />
<b>Brief Intro:</b><br /><br />
Tianhao Zhang is a junior undergraduate majoring in biophysics. However he didn’t have much biology training because of the junior grade. He is responsible for the little java application which is independence from our major program. He also helped Xinyu Liao to build the modle of network and find suitable equation used in network analysis.<br /><br />
<br />
Besides biology, He is also interested in computer science so he studied dual degree in school. He think iGEM software is a great program to combine computer science and biology together. That’s why he joined in and became a member of us.<br />
</p><br />
</div><br />
</div><br />
<br />
<div id="chen" class="box"></div><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/3/32/USTC-Software_Shaochen.png" /></div><br />
<div class="words"><br />
<p><br />
<b>Name</b>: Chen Shao (邵辰)<br /><br />
<b>Age</b>: 19<br /><br />
<b>Major</b>:Mathematical Sciences<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Division of Labor</b>: Algorithm and coding<br /><br />
<b>Email</b>:shaochen001@gmail.com<br /><br />
<b>Brief Intro:</b><br /><br />
Chen Shao, as a junior student major in mathematics, is interested in learning in and out of major area. Without training in biology, she just joined in with passion and persistence. She is participating in the later stage of the genetic network application, such as calculating for an optimal solution of the gene. In her job, she combined classical algorithms and our networks’ features, in order to optimize the codes and results. In her eyes, it is a happy and enjoyable process with excellent partners.<br />
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<p id="h1">Instructors</p><br />
<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/f/f6/USTC-Software_Lui.png" /></div><br />
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<b>name</b>:Haiyan Liu<br /><br />
Professor, School of Life Sciences University of Science & Technology of China<br /><br />
<b>Email</b>: hyliu@ustc.edu.cn<br /><br />
<b>Brief Intro</b><br /><br />
Haiyan Liu was born in Sichuan Province, China. He received his BS degree in Biology in 1990 and PhD degree in Biochemistry and Molecular Biology in 1996, both from USTC. Between 1993 and 1995 he was a visiting graduate student in Laboratory of Physical Chemistry of ETH, Zurich (Switzerland). From 1998 to 2000 he was post-doctoral research associate at Department of Chemistry, Duke University (USA) and Department of Biochemistry and Biophysics UNC-Chapel Hill (USA). Since 2001, he has been a professor of computational biology at School of Life Sciences, USTC.<br />
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<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/a/a3/USTC-Software_Hong.png" /></div><br />
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<b>name</b>:Jong Hong<br /><br />
Professor, School of Life Sciences University of Science & Technology of China<br /><br />
<b>Email</b>: hjiong@ustc.edu.cn<br /><br />
<b>Brief Intro:</b><br /><br />
Jiong Hong was born in Anhui Province, China. He received his BS degree in 1993 from Anhui Normal University, MSc degree in 1996 from Beijing Normal University, and Ph.D degree in 2003 from Kyoto University, all in Life Sciences. From 2006 to 2008, he did post-doctoral research in Virginia Tech University, USA. Since 2008, he has been introduced to the USTC as talented.<br />
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<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/8/8d/USTC-Software_Zhi.png" /></div><br />
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<b>name</b>:Zhi Liang<br /><br />
<b>Email</b>:liangzhi@ustc.edu.cn<br /><br />
<b>Brief Intro:</b><br /><br />
Liang Zhi, Ph.D., Associate Professor Extraordinary . 2000 graduated from the school of Life Sciences in the University of Science and Technology of China, received bachelor's degree in biology and electronics and information engineering dual degree. 2006 graduated from the school of Life Sciences in the University of Science and Technology of China protein crystallography laboratory , PhD. From 2007 to 2012 in the school of Life Sciences in the University of Science and Technology of China Systems Biology Laboratory in systems biology postdoctoral research. Postdoctoral period has won China Postdoctoral Science Foundation funded second and Natural Science Foundation funding. 2012 University of Science and Technology of China Extraordinary Life Sciences Research Associate . Research results published on Nucleic Acids Res, Bioinformatics, Bioinformatics, J Biol Chem.<br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/teamTeam:USTC-Software/team2013-10-27T15:59:58Z<p>USTCkun: </p>
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<b>Name</b>: Chenkun Wang(王晨坤)<br /><br />
<b>Age</b>: 21 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br />
<b>Major</b>: Condensed Matter Physics<br /><br />
<b>Grade</b>: Senior Undergraduate<br /><br />
<b>Division of labor</b>: Basic construction of software, software coding, GUI<br /><br />
<b>Email</b>: ustckun@gamil.com<br /><br />
<b>Brief Intro:</b><br />
Chenkun Wang is a senior undergraduate majoring in condensed matter physics. As a team leader of USTC-Software, he participated in nearly every part of project. He not only accomplished the basic construction of software, but also was responsible for the development of starter module as well as the database fetching coding. What's more, he completed the GUI of our software with the help of Shuai Ma.<br />
After nearly half year's working for the competition of iGEM_2013_Software, he became overwhelming interested in command line and simulation. He will graduate next year and he hopes to continue such work in his future studies. His dream is to pursue a Ph.D. in Electrical Engineering.</p><br />
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<div id="yang" class="box"></div><br />
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<b>Name</b>: Jinyang Li (李进阳)<br /><br />
<b>Age</b>: 21 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br />
<b>Major</b>: Theoretical Physics<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Division of labor</b>: Software coding, design<br /><br />
<b>Email</b>: jinyangustc@gmail.com<br /><br />
<b>Brief Intro:</b><br />
Jinyang Li is a junior undergraduate majoring in Theoretical Physics. He is responsible for the development of network analysis module. He has reviewed some key literatures to provide theoretical support for the project. He also designed the prediction algorithm together with Chenkun Wang and improved it. In the development process, Jinyang has found an interesting phenomenon in the gene regulation network of Escherichia coli and prepared to study it further. He also made contribution to art design of the project.<br /><br />
At school, he is a director of a volunteer organization. Because of the work of the iGEM competition, he got interested in complex network and prepared to join the Nonlinear Science and Complex System Center(NLSC) of USTC.</p><br />
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<div id="pei" class="box"></div><br />
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<b>Name</b>:Pei Zhang (张沛)<br /><br />
<b>Age</b>:19<br /><br />
<b>Major</b>:Computer Sciences and Technology<br /><br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:team manager<br /><br />
<b>Email</b>: zp33@mail.ustc.edu.cn<br /><br />
<b>Breif Intro:</b><br /><br />
Pei Roc Zhang is a sophomore undergraduate from the School of Computer Science and Technology. When joining the iGem_2013_Software_Team, he was a freshman undergraduate from the School of Life Sciences who loved CS. As a member of the team, he participates in software development and modifies English documents. What's more, as the manager of the team, he is in charge of all the fincial work and other small chores.<br /> <br />
As a sophomore who has just transfered to CS department, he is now struggling for a better academic performance and seeking for offers from top research facilities in the world in the near two years. </p><br />
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<b>Name</b>: Xingyu Liao (廖星宇)<br /><br />
<b>Age</b>: 19<br /><br />
<b>Major</b>: Theoretical Physics<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Email</b>: liaoxyn@gmail.com<br /><br />
<b>Brief Info :</b><br /><br />
He was in Department of Earth and Space Sciences when he was a freshman. He transferred to Department of Physics after the first academic year. He joined the USTC_software team out of interests in CS and was in charge of genetic network calculation division. The experience of working in igem team is really exciting, for he met a lot of people here and they worked as a team. He also learnt something new by himself and had a new vision on biology.<br />
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<div class="members"><br />
<div class="pic"><img src="https://static.igem.org/mediawiki/2013/c/cd/USTC-Software_Shaohua.png" /></div><br />
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<b>Name</b>: Shaohua Chen (陈少华)<br /><br />
<b>Major</b>:Nuclear Science and technology <br /><br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:wiki<br /><br />
<b>Email</b>: csh66@mail.ustc.edu.cn<br /><br />
<b>Brief Intro:</b><br /><br />
Hello,I am Shaohua Chen ,a sophomore undergraduate in USTC. Before joining in 2013 USTC-Software ,I knew little about the Internet.To change this bad situation ,I was determined to join in USTC-Software where I could not only learn Internet but also know more biology.So it's an honour for me to cooperate with many gifted and responsible teammates ,from whom I learned a lot. In the team , I am in charge of the wiki,during the process ,I find that a person's ability is limited,teamwork makes our work more efficient. <br />
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<p><br />
<b>Name</b>:Jinjing Yi (易津晶)<br /><br />
<b>Age</b>:20<br /><br />
<b>Major</b>:Theoretical Physics<br /><br />
<b>Grade</b>:Junior Undergraduate<br /><br />
<b>Division of labor</b>:PSO, design, diary, Human Practice<br /><br />
<b>Email</b>: 1195036879@qq.com<br /><br />
Jinjing Yi is a junior undergraduate majoring in Theoretical Physics. She participated in the prediction part and finished the first version of partical swarm opimization algorithm. She is also responsible for diary writing and Human Practice.During this summer, she became interested in photoshop and helped design the team logo. Joining in this happy team made her more confident and more energetic.<br /><br />
She used to be a member in student union and meanwhile focused on her academic learning. In the last two years, she decided to join in the research in theory of condensed matter physics.<br />
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<b>Name</b>:Haosen Tan (谈浩森)<br /><br />
<b>Age</b>:20 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br />
<b>Grade</b>: Sophomore undergraduate<br /><br />
<b>Major</b>: Physics <br /> <br />
<b>Division of Labor:</b><br />
Software coding, debugging and releasing the software, tutorial<br /><br />
<b>Email</b>: ths@mail.ustc.edu.cn<br /><br />
<b>Brief Intro</b>: <br />
A student from school of physics with much debug experience and debug skill. when joining the team, he was a freshman undergraduate. <br />
As a member of the team, he participates in software development and writes some English documents, for example, the tutorial and Quikstart. What's more, as the one of programmers of the team, he is in charge of debugging and releasing the software on different platform such as Windows, Mac OS X, Linux.<br /> <br />
He is now struggling for a better academic platform to learn something and share something. In addition, he firmly believes USTC-Software will do a good job in the Asia jamboree and World jamboree!<br />
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<div class="members"><br />
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<b>Name</b> : Shuai Ma (马帅)<br /><br />
<b>Major</b> : Nuclear Science and Technology<br /><br />
<b>Grade</b> : Junior Undergraduate<br /><br />
<b>Division of labor </b>: GUI<br /><br />
<b>Email</b>: ms369@mail.ustc.edu.cn<br /><br />
<b>Breif Intro :</b><br /><br />
Shuai Ma is a junior undergraduate from the School of Nuclear Science and Technology. In the team he is in charge of designing GUI with Qt . With little knowledge in C++, the job was a real challenge for him, yet he accomplished it with great determination and dedication. In everyday life, he enjoys all kinds of ball games while soccer ball is his favorite. <br />
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<b>Name</b>:Monan Zhou (周墨南)<br /><br />
<b>Age</b>:20<br /><br />
<b>Major</b>:Mathematical Sciences<br /> <br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:Wiki<br /><br />
<b>Email</b>: zmn2012@mail.ustc.edu.cn<br /><br />
<b>Brief Intro</b>:<br /><br />
Monan Zhou is a sophomore undergraduate from the School of Mathematical Sciences. When joining the 2013 USTC-Software, he was a freshman undergraduate from the School of Life Sciences who loved Math. As a member of the team, he participates in wiki.<br />
As a sophomore who has just transferred to School of Mathematical Sciences, he is now struggling for a better academic performance and seeking for offers from top research facilities in the world in the near two years. <br />
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<b>Name</b>:Hao Xu (徐浩)<br /><br />
<b>Age</b>:19<br /><br />
<b>Major</b>:Phisical Sciences<br /><br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:Data visualization<br /><br />
<b>Email</b>: xuhao1@163.com<br /><br />
<b>Breif Intro:</b><br /><br />
Hao Xu is always interested in lots of things,such as programming,airplane models and so on.Hao was in charge of the visualizition of data with java.In USTC-Software,he find that biology is even insteresting and he learned a lot.By the way ,He is too lazy to write a self intro...<a href="http://www.stlover.org">learn more</a><br />
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<b>Name</b>: Tianhao Zhang (张天昊)<br /><br />
<b>Age</b>: 20<br /><br />
<b>Major</b>: Biophysics<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Division of labor</b>: Clotho App<br /><br />
<b>Email</b>: zhth1993@gmail.com<br /><br />
<b>Brief Intro:</b><br /><br />
Tianhao Zhang is a junior undergraduate majoring in biophysics. However he didn’t have much biology training because of the junior grade. He is responsible for the little java application which is independence from our major program. He also helped Xinyu Liao to build the modle of network and find suitable equation used in network analysis.<br /><br />
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Besides biology, He is also interested in computer science so he studied dual degree in school. He think iGEM software is a great program to combine computer science and biology together. That’s why he joined in and became a member of us.<br />
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<b>Name</b>: Chen Shao (邵辰)<br /><br />
<b>Age</b>: 19<br /><br />
<b>Major</b>:Mathematical Sciences<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Division of Labor</b>: Algorithm and coding<br /><br />
<b>Email</b>:shaochen001@gmail.com<br /><br />
<b>Brief Intro:</b><br /><br />
Chen Shao, as a junior student major in mathematics, is interested in learning in and out of major area. Without training in biology, she just joined in with passion and persistence. She is participating in the later stage of the genetic network application, such as calculating for an optimal solution of the gene. In her job, she combined classical algorithms and our networks’ features, in order to optimize the codes and results. In her eyes, it is a happy and enjoyable process with excellent partners.<br />
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<p id="h1">Instructors</p><br />
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<b>name</b>:Haiyan Liu<br /><br />
Professor, School of Life Sciences University of Science & Technology of China<br /><br />
<b>Email</b>: hyliu@ustc.edu.cn<br /><br />
<b>Brief Intro</b><br /><br />
Haiyan Liu was born in Sichuan Province, China. He received his BS degree in Biology in 1990 and PhD degree in Biochemistry and Molecular Biology in 1996, both from USTC. Between 1993 and 1995 he was a visiting graduate student in Laboratory of Physical Chemistry of ETH, Zurich (Switzerland). From 1998 to 2000 he was post-doctoral research associate at Department of Chemistry, Duke University (USA) and Department of Biochemistry and Biophysics UNC-Chapel Hill (USA). Since 2001, he has been a professor of computational biology at School of Life Sciences, USTC.<br />
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<b>name</b>:Jong Hong<br /><br />
Professor, School of Life Sciences University of Science & Technology of China<br /><br />
<b>Email</b>: hjiong@ustc.edu.cn<br /><br />
<b>Brief Intro:</b><br /><br />
Jiong Hong was born in Anhui Province, China. He received his BS degree in 1993 from Anhui Normal University, MSc degree in 1996 from Beijing Normal University, and Ph.D degree in 2003 from Kyoto University, all in Life Sciences. From 2006 to 2008, he did post-doctoral research in Virginia Tech University, USA. Since 2008, he has been introduced to the USTC as talented.<br />
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<div class="pic"><img src="https://static.igem.org/mediawiki/2013/8/8d/USTC-Software_Zhi.png" /></div><br />
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<b>name</b>:Zhi Liang<br /><br />
<b>Email</b>:liangzhi@ustc.edu.cn<br /><br />
<b>Brief Intro:</b><br /><br />
Liang Zhi, Ph.D., Associate Professor Extraordinary . 2000 graduated from the school of Life Sciences in the University of Science and Technology of China, received bachelor's degree in biology and electronics and information engineering dual degree. 2006 graduated from the school of Life Sciences in the University of Science and Technology of China protein crystallography laboratory , PhD. From 2007 to 2012 in the school of Life Sciences in the University of Science and Technology of China Systems Biology Laboratory in systems biology postdoctoral research. Postdoctoral period has won China Postdoctoral Science Foundation funded second and Natural Science Foundation funding. 2012 University of Science and Technology of China Extraordinary Life Sciences Research Associate . Research results published in Nucleic Acids Res, Bioinformatics, BMC Bi<br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/teamTeam:USTC-Software/team2013-10-27T15:55:46Z<p>USTCkun: </p>
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<b>Name</b>: Chenkun Wang(王晨坤)<br /><br />
<b>Age</b>: 21 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br />
<b>Major</b>: Condensed Matter Physics<br /><br />
<b>Grade</b>: Senior Undergraduate<br /><br />
<b>Division of labor</b>: Basic construction of software, software coding, GUI<br /><br />
<b>Email</b>: ustckun@gamil.com<br /><br />
<b>Brief Intro:</b><br />
Chenkun Wang is a senior undergraduate majoring in condensed matter physics. As a team leader of USTC-Software, he participated in nearly every part of project. He not only accomplished the basic construction of software, but also was responsible for the development of starter module as well as the database fetching coding. What's more, he completed the GUI of our software with the help of Shuai Ma.<br />
After nearly half year's working for the competition of iGEM_2013_Software, he became overwhelming interested in command line and simulation. He will graduate next year and he hopes to continue such work in his future studies. His dream is to pursue a Ph.D. in Electrical Engineering.</p><br />
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<div id="yang" class="box"></div><br />
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<p><br />
<b>Name</b>: Jinyang Li (李进阳)<br /><br />
<b>Age</b>: 21 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br />
<b>Major</b>: Theoretical Physics<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Division of labor</b>: Software coding, design<br /><br />
<b>Email</b>: jinyangustc@gmail.com<br /><br />
<b>Brief Intro:</b><br />
Jinyang Li is a junior undergraduate majoring in Theoretical Physics. He is responsible for the development of network analysis module. He has reviewed some key literatures to provide theoretical support for the project. He also designed the prediction algorithm together with Chenkun Wang and improved it. In the development process, Jinyang has found an interesting phenomenon in the gene regulation network of Escherichia coli and prepared to study it further. He also made contribution to art design of the project.<br /><br />
At school, he is a director of a volunteer organization. Because of the work of the iGEM competition, he got interested in complex network and prepared to join the Nonlinear Science and Complex System Center(NLSC) of USTC.</p><br />
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<div id="pei" class="box"></div><br />
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<p><br />
<b>Name</b>:Pei Zhang (张沛)<br /><br />
<b>Age</b>:19<br /><br />
<b>Major</b>:Computer Sciences and Technology<br /><br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:team manager<br /><br />
<b>Email</b>: zp33@mail.ustc.edu.cn<br /><br />
<b>Breif Intro:</b><br /><br />
Pei Roc Zhang is a sophomore undergraduate from the School of Computer Science and Technology. When joining the iGem_2013_Software_Team, he was a freshman undergraduate from the School of Life Sciences who loved CS. As a member of the team, he participates in software development and modifies English documents. What's more, as the manager of the team, he is in charge of all the fincial work and other small chores.<br /> <br />
As a sophomore who has just transfered to CS department, he is now struggling for a better academic performance and seeking for offers from top research facilities in the world in the near two years. </p><br />
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<div id="yu" class="box"></div><br />
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<p><br />
<b>Name</b>: Xingyu Liao (廖星宇)<br /><br />
<b>Age</b>: 19<br /><br />
<b>Major</b>: Theoretical Physics<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Email</b>: liaoxyn@gmail.com<br /><br />
<b>Brief Info :</b><br /><br />
He was in Department of Earth and Space Sciences when he was a freshman. He transferred to Department of Physics after the first academic year. He joined the USTC_software team out of interests in CS and was in charge of genetic network calculation division. The experience of working in igem team is really exciting, for he met a lot of people here and they worked as a team. He also learnt something new by himself and had a new vision on biology.<br />
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<div id="hua" class="box"></div><br />
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<p><br />
<b>Name</b>: Shaohua Chen (陈少华)<br /><br />
<b>Major</b>:Nuclear Science and technology <br /><br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:wiki<br /><br />
<b>Email</b>: csh66@mail.ustc.edu.cn<br /><br />
<b>Brief Intro:</b><br /><br />
Hello,I am Shaohua Chen ,a sophomore undergraduate in USTC. Before joining in 2013 USTC-Software ,I knew little about the Internet.To change this bad situation ,I was determined to join in USTC-Software where I could not only learn Internet but also know more biology.So it's an honour for me to cooperate with many gifted and responsible teammates ,from whom I learned a lot. In the team , I am in charge of the wiki,during the process ,I find that a person's ability is limited,teamwork makes our work more efficient. <br />
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<div id="jing" class="box"></div><br />
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<div class="pic"><img src="https://static.igem.org/mediawiki/2013/6/6a/USTC-Software_Jing.png"/></div><br />
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<p><br />
<b>Name</b>:Jinjing Yi (易津晶)<br /><br />
<b>Age</b>:20<br /><br />
<b>Major</b>:Theoretical Physics<br /><br />
<b>Grade</b>:Junior Undergraduate<br /><br />
<b>Division of labor</b>:PSO, design, diary, Human Practice<br /><br />
<b>Email</b>: 1195036879@qq.com<br /><br />
Jinjing Yi is a junior undergraduate majoring in Theoretical Physics. She participated in the prediction part and finished the first version of partical swarm opimization algorithm. She is also responsible for diary writing and Human Practice.During this summer, she became interested in photoshop and helped design the team logo. Joining in this happy team made her more confident and more energetic.<br /><br />
She used to be a member in student union and meanwhile focused on her academic learning. In the last two years, she decided to join in the research in theory of condensed matter physics.<br />
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<div class="pic"><img src="https://static.igem.org/mediawiki/2013/5/5e/USTC-Software_Haosen.png" /></div><br />
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<p><br />
<b>Name</b>:Haosen Tan (谈浩森)<br /><br />
<b>Age</b>:20 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br />
<b>Grade</b>: Sophomore undergraduate<br /><br />
<b>Major</b>: Physics <br /> <br />
<b>Division of Labor:</b><br />
Software coding, debugging and releasing the software, tutorial<br /><br />
<b>Email</b>: ths@mail.ustc.edu.cn<br /><br />
<b>Brief Intro</b>: <br />
A student from school of physics with much debug experience and debug skill. when joining the team, he was a freshman undergraduate. <br />
As a member of the team, he participates in software development and writes some English documents, for example, the tutorial and Quikstart. What's more, as the one of programmers of the team, he is in charge of debugging and releasing the software on different platform such as Windows, Mac OS X, Linux.<br /> <br />
He is now struggling for a better academic platform to learn something and share something. In addition, he firmly believes USTC-Software will do a good job in the Asia jamboree and World jamboree!<br />
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<div id="shuai" class="box"></div><br />
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<p><br />
<b>Name</b> : Shuai Ma (马帅)<br /><br />
<b>Major</b> : Nuclear Science and Technology<br /><br />
<b>Grade</b> : Junior Undergraduate<br /><br />
<b>Division of labor </b>: GUI<br /><br />
<b>Email</b>: ms369@mail.ustc.edu.cn<br /><br />
<b>Breif Intro :</b><br /><br />
Shuai Ma is a junior undergraduate from the School of Nuclear Science and Technology. In the team he is in charge of designing GUI with Qt . With little knowledge in C++, the job was a real challenge for him, yet he accomplished it with great determination and dedication. In everyday life, he enjoys all kinds of ball games while soccer ball is his favorite. <br />
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<b>Name</b>:Monan Zhou (周墨南)<br /><br />
<b>Age</b>:20<br /><br />
<b>Major</b>:Mathematical Sciences<br /> <br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:Wiki<br /><br />
<b>Email</b>: zmn2012@mail.ustc.edu.cn<br /><br />
<b>Brief Intro</b>:<br /><br />
Monan Zhou is a sophomore undergraduate from the School of Mathematical Sciences. When joining the 2013 USTC-Software, he was a freshman undergraduate from the School of Life Sciences who loved Math. As a member of the team, he participates in wiki.<br />
As a sophomore who has just transferred to School of Mathematical Sciences, he is now struggling for a better academic performance and seeking for offers from top research facilities in the world in the near two years. <br />
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<b>Name</b>:Hao Xu (徐浩)<br /><br />
<b>Age</b>:19<br /><br />
<b>Major</b>:Phisical Sciences<br /><br />
<b>Grade</b>:Sophomore Undergraduate<br /><br />
<b>Division of labor</b>:Data visualization<br /><br />
<b>Email</b>: xuhao1@163.com<br /><br />
<b>Breif Intro:</b><br /><br />
Hao Xu is always interested in lots of things,such as programming,airplane models and so on.Hao was in charge of the visualizition of data with java.In USTC-Software,he find that biology is even insteresting and he learned a lot.By the way ,He is too lazy to write a self intro...<a href="http://www.stlover.org">learn more</a><br />
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<div class="pic"><img src="https://static.igem.org/mediawiki/2013/b/b2/USTC-SOftware-Hao.png" /></div><br />
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<p><br />
<b>Name</b>: Tianhao Zhang (张天昊)<br /><br />
<b>Age</b>: 20<br /><br />
<b>Major</b>: Biophysics<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Division of labor</b>: Clotho App<br /><br />
<b>Email</b>: zhth1993@gmail.com<br /><br />
<b>Brief Intro:</b><br /><br />
Tianhao Zhang is a junior undergraduate majoring in biophysics. However he didn’t have much biology training because of the junior grade. He is responsible for the little java application which is independence from our major program. He also helped Xinyu Liao to build the modle of network and find suitable equation used in network analysis.<br /><br />
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Besides biology, He is also interested in computer science so he studied dual degree in school. He think iGEM software is a great program to combine computer science and biology together. That’s why he joined in and became a member of us.<br />
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<div id="chen" class="box"></div><br />
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<div class="pic"><img src="https://static.igem.org/mediawiki/2013/3/32/USTC-Software_Shaochen.png" /></div><br />
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<p><br />
<b>Name</b>: Chen Shao (邵辰)<br /><br />
<b>Age</b>: 19<br /><br />
<b>Major</b>:Mathematical Sciences<br /><br />
<b>Grade</b>: Junior Undergraduate<br /><br />
<b>Division of Labor</b>: Algorithm and coding<br /><br />
<b>Email</b>:shaochen001@gmail.com<br /><br />
<b>Brief Intro:</b><br /><br />
Chen Shao, as a junior student major in mathematics, is interested in learning in and out of major area. Without training in biology, she just joined in with passion and persistence. She is participating in the later stage of the genetic network application, such as calculating for an optimal solution of the gene. In her job, she combined classical algorithms and our networks’ features, in order to optimize the codes and results. In her eyes, it is a happy and enjoyable process with excellent partners.<br />
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<p id="h1">Instructors</p><br />
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<div class="pic"><img src="https://static.igem.org/mediawiki/2013/f/f6/USTC-Software_Lui.png" /></div><br />
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<p><br />
<b>name</b>:Haiyan Liu<br /><br />
Professor, School of Life Sciences University of Science & Technology of China<br /><br />
<b>Email</b>: hyliu@ustc.edu.cn<br /><br />
<b>Brief Intro</b><br /><br />
Haiyan Liu was born in Sichuan Province, China. He received his BS degree in Biology in 1990 and PhD degree in Biochemistry and Molecular Biology in 1996, both from USTC. Between 1993 and 1995 he was a visiting graduate student in Laboratory of Physical Chemistry of ETH, Zurich (Switzerland). From 1998 to 2000 he was post-doctoral research associate at Department of Chemistry, Duke University (USA) and Department of Biochemistry and Biophysics UNC-Chapel Hill (USA). Since 2001, he has been a professor of computational biology at School of Life Sciences, USTC.<br />
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<div class="pic"><img src="https://static.igem.org/mediawiki/2013/a/a3/USTC-Software_Hong.png" /></div><br />
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<p><br />
<b>name</b>:Jong Hong<br /><br />
Professor, School of Life Sciences University of Science & Technology of China<br /><br />
<b>Email</b>: hjiong@ustc.edu.cn<br /><br />
<b>Brief Intro:</b><br /><br />
Jiong Hong was born in Anhui Province, China. He received his BS degree in 1993 from Anhui Normal University, MSc degree in 1996 from Beijing Normal University, and Ph.D degree in 2003 from Kyoto University, all in Life Sciences. From 2006 to 2008, he did post-doctoral research in Virginia Tech University, USA. Since 2008, he has been introduced to the USTC as talented.<br />
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<div class="pic"><img src="https://static.igem.org/mediawiki/2013/8/8d/USTC-Software_Zhi.png" /></div><br />
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<p><br />
<b>name</b>:Zhi Liang<br /><br />
<b>Email</b>:liangzhi@ustc.edu.cn<br /><br />
<b>Brief Intro:</b><br /><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/MethodTeam:USTC-Software/Project/Method2013-10-27T14:13:05Z<p>USTCkun: </p>
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<h1 align="justify">Methodologies</h1><br />
<p align="justify">In order to simulate the GRN's working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</br><br/><br />
There are four parts of methodologies: Database, Operon Theory and Regulatory Model, Forward Analysis and Reverse Analysis.<br />
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<h2>Database</h2><br />
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<div class="jobs_trigger"><strong>Abstract</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">To simulate and analyze a genetic regulatory network (GRN), we need to build an objects' array to store the complete information of each gene. It contains regulation relationships between genes, sequences of genes, sequences of promoters and so on. However, it's hard to find an appropriate database online containing all information we need in a simple file. RegulonDB has downloadable files about the regulation between transcription factors (TF) and genes. Files about genetic information, transcription unit information and promoter information can also be downloaded from the RegulonDB. All those files have been put into file “source data” in the root directory of our software. They contain all information the simulation needs and we use fetching module to achieve data extraction and integration. There are four steps: fetch regulation relationships, fetch gene information, fetch promoter information and integrate information above.<br />
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<div class="jobs_trigger"><strong>Fetch Regulation</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">In GRN, there are two kinds of files: <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_tf.txt"> TF to TF</a> and <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_gene.txt">TF to Gene</a>. Since the database about the regulation between TFs and Genes contains only one-way interaction, the matrix of GRN is a rectangle.</br></br><br />
First of all, read the regulation relationship of TFs. Our software filters the documentation of RegulonDB on the head of all files and then reads the name of regulate and regulated TF, which is also the name of its genes, one by one. In the same time, our software numerates the genes and stores their names into an objects' array of genetic data. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/69/USTC_Software_TT.jpg"/><br />
The regulation of TFs has been put into a square matrix whose row is the regulator and column is the one regulated by. To make our GRN as complete as possible, the regulation between TF and genes has joined into the matrix. The one-way interaction results that we must read the TF in order to fulfill the regulator before completing the TF to gene's regulation in the same way of TF to TF. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/47/USTC_Software_TG.jpg"/><br />
At last, a regulatory matrix whose row represents regulate gene (TF) and whose column represents gene regulated by (TF+Gene) has been output into a file called “old_GRN” in root directory. The values in GRN matrix are regulations in which “1” means positive activation, “-1” means repression and “0” means no relationship. There have been some regulations both positive and negative identified regulations are determined by the experimental environment. As a result, our software picks out those uncertain genes and stores them into a file named “uncertain_database”.</br></br><br />
&nbsp;&nbsp;The format of uncertain database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;? &nbsp;&nbsp;&nbsp;Gene_name->Gene_name</br></br><br />
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The question mark represents the unknown regulation between regulator and regulated-by whose names presented afterward. Users could replace the question mark with the data known in past experiment. (“+” rep positive, “-” rep negative). Our software will replace the values in matrix automatically. But if not rewrote, our software will regard those regulation as unknown.<br />
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<div class="jobs_trigger"><strong> Fetch Gene Info</strong></div><br />
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All gene information has been deposited into a file named gene_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/Gene_sequence.txt">here</a>. In order of picking out the genes in GRN as fast as possible, all genetic information are stored in a “map”. “Map” is just like a dictionary yet its words are names of genes and its descriptions of words are replaced by genetic information. By using binary tree method, it is very fast to search the “word” wanted in the “dictionary”. As tested, the speed of binary tree method built-in “map” function is 720 times faster than traversal method.</br></br><br />
&nbsp;&nbsp;The format of Gene Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;Left_end_position &nbsp;&nbsp;&nbsp;Right_end_position &nbsp;&nbsp;&nbsp;DNA_strand &nbsp;&nbsp;&nbsp;Product_type &nbsp;&nbsp;&nbsp;&nbsp;Product_name &nbsp;&nbsp;&nbsp;Start_codon_sequence&nbsp;&nbsp;&nbsp; Stop_codon_sequence &nbsp;&nbsp;&nbsp;Gene_sequence</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/45/USTC_Software_GI.jpg"/><br />
The label of the map vector is gene name which will be picked out based on the names read in regulation matrix before. It is really fast using the binary tree method to find the specific genetic information and store them into a specific object. Those information includes gene ID, left position, right position, gene description and gene sequence. The gene ID is used to link to RegulonDB's gene details; The left position is used to find its specific transcription unit; The right position is used to figure out the base amount; The description of genes is used to distinguish the RNA and protein; The sequence is used to predict the regulation by alignment.<br />
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<br />
<div class="jobs_trigger"> <strong>Fetch Promoter Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">All promoter information has been deposited into a file named promoter_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/PromoterSet.txt">here</a>. But we also need transcription unit information because the information files about promoter do not contain all genes' names backward. “TU Info” file, which can be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/TUSet.txt">here</a>, contains the starting position of each TU and its promoter name. Our software picks out the starting position into a integer array. Using the left position picked out in gene info, our software would find out which unit the gene belongs to through dichotomy method and then stores the name of promoter into corresponding object.</br></br><br />
&nbsp;&nbsp;The format of TU info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Operon_name &nbsp;&nbsp;&nbsp;Unit_name &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;Transcription_start_site ......</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/1/1e/USTC_Software_TI.jpg"/><br />
The principle of fetching information of promoters is same as fetching genes's. Our software stores the promoter information from the file named “promoter_info” in a “map” which could be used to pick out the promoter sequence by searching promoter name through binary tree method.</br></br><br />
&nbsp;&nbsp;The format of Promoter Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Promoter_ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Promoter_name</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/8/8a/USTC_Software_PI.jpg"/><br />
The sequence of promoter will be used in the alignment method in next module which could make a prediction of exogenous genes' regulation pattern.<br />
</p> </div> <br />
<br />
<br />
<br />
<div class="jobs_trigger"> <strong>Integration</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify"> <br />
Our software integrates all information we picked out about genes and generates a file named “all_info” —— all information about genes —— for the output graphical interface's reading. In the meanwhile, the array of objects containing all information has been stored in computer memory which greatly improve the computing speed of our software.</br></br><br />
&nbsp;&nbsp;The format of all_info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;No. &nbsp;&nbsp;&nbsp;promoter_sequence &nbsp;&nbsp;&nbsp;gene_sequence &nbsp;&nbsp;&nbsp;gene_name &nbsp;&nbsp;&nbsp;ID &nbsp;&nbsp;&nbsp;left_position &nbsp;&nbsp;&nbsp;right_position &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;&nbsp;description</br><br />
<br />
The fetching module generates three files: old_GRN, all_info and uncertain_database.</br><br />
</p><br />
</div><br />
<br />
<br />
</div><!--jobs container--><br />
<br />
<br />
</div><br />
<br />
<br />
<br />
<br />
<div id="Alignment_Analyze"><br />
<h2>Operon Theory and Regulatory Model</h2><br />
<br />
<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Operon Theory</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><br />
<p align="justify">In genetics, an operon is a functioning unit of genomic DNA containing a cluster of genes<br />
under the control of a single regulatory signal or promoter. The genes contained in the<br />
operon are either expressed together or not at all. Several genes must be both cotranscribed<br />
and co-regulated to define an operon.<br /><br /><br />
The first time "operon" was proposed is in a paper of French Academic Science, 1960.<br />
The lac operon of the model bacterium E. coli was discovered and provides a typical<br />
example of operon function. It consists a promoter, an operator, three structural genes and<br />
a terminator. The operon is regulated by several factors including the availability of glucose<br />
and lactose.<br /><br /><br />
From this paper, the so-called general theory of the operon was developed. According to<br />
the theory, all genes are controlled by means of operons through a single feedback<br />
regulatory mechanism-repression. The first operon to be described was the lac operon in<br />
E. coli. The 1965 Nobel Prize in Physiology and Medicine was awarded to François Jacob,<br />
André Michel Lwoff and Jacques Lucien Monod for their discoveries concerning the operon and virus synthesis.<br /><br />
</p><br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/7/7d/USTC_Software_Figure_1.png" /><br />
<p align="center"><strong>Figure 1.</strong> Structure of Operon</p></div><br />
<p align="justify">An operon is made up of several structural genes arranged under a common promoter and<br />
regulated by a common operator. It is defined as a set of adjacent structural genes, plus<br />
the adjacent regulatory signals that affect transcription of the structural genes. The<br />
regulators of a given operon, including repressors, corepressors and activators, are not<br />
necessarily coded for by that operon.<br /><br /><br />
As a unit of transcription, upstream of the structural genes lies a promoter sequence which<br />
provides a site for RNA polymerase to bind and initiate transcription. Close to the promoter<br />
lies a section of DNA called an operator.<br /><br /><br />
Operon regulation can be either negative or positive by induction or repression. Negative<br />
control involves the binding of a repressor to the operator to prevent transcription.<br />
Operons can also be positively controlled. An activator protein binds to DNA, usually at a<br />
site other than the operator, to stimulate transcription.<br />
</p><br />
<div align="center"><img style="width:600px;" src="https://static.igem.org/mediawiki/igem.org/2/25/USTC_Software_Figure_2.png"/><br />
<p align="justify"><strong>Figure 2.</strong> Regulation of Operon<br />
1: RNA Polymerase, 2: Repressor, 3: Promoter, 4: Operator, 5: Lactose, 6: lacZ, 7:<br />
lacY, 8: lacA. Top: The gene is essentially turned off. There is no lactose to inhibit the<br />
repressor, so the repressor binds to the operator, which obstructs the RNA polymerase<br />
from binding to the promoter and making lactase.Bottom: The gene is turned on.Lactose<br />
is inhibiting the repressor, allowing the RNA polymerase to bind with the promoter, and<br />
express the genes, which synthesize lactase. Eventually, the lactase will digest all of the<br />
lactose, until there is none to bind to the repressor. The repressor will then bind to the<br />
operator, stopping the manufacture of lactase.</p></div><br />
<br />
<br />
</div><br />
<br />
<div class="jobs_trigger"><strong>Regulatory Model</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">Regulation of gene expression includes four levels. We choose the transcriptional level to simulate the regulation both for its significance and model simplification.</p><br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/igem.org/8/87/USTC_Software_Figure_3.png" /><br />
<p><strong>Figure 3.</strong>Regulation of gene expression.<br />Our regulation model is built based on the operon theory.<br /> The promoter region is regarded as the main regulatory region.</p></div><br />
</div><br />
<br />
<br />
<br />
<div class="jobs_trigger"> <strong>Similarity and Homology</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment. It is defined as the proportion of the common subsequence in the aligned sequence. Any two sequences share a certain<br />
similarity. It should be noted that similarity and homology are two different concepts.<br /><br /><br />
As with anatomical structures, homology between protein or DNA sequences is defined in<br />
terms of shared ancestry. Two segments of DNA can have shared ancestry because of<br />
either a speciation event or a duplication event. The terms “percent homology” and<br />
“sequence similarity” are often used interchangeably. As with anatomical structures, high<br />
sequence similarity might occur because of convergent evolution, or, as with shorter<br />
sequences, because of chance. Such sequences are similar but not homologous.<br />
Sequence regions that homologous are also called conserved.<br /><br /><br />
In our project, we use similarity to connect the exogenous gene with the original network.<br />
Because there is a good chance that the exogenous gene is not homologous with the<br />
genes in the network.</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The GRN matrix is the mathematical description of gene regulatory network in which “1” represents “enhance”, “-1” represents “repress” and “0” represents “no regulatory relationship”. The units(RU) in x-axis regulate the units in y-axis. A row can be seen as a vector containing all the information of the target(corresponding unit in the y-axis). Similarly, a column can be seen as a vector containing all the information of the regulator(corresponding unit in the x-axis).</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment based on Needleman-Wunsch algorithm[FIXME: wiki link here]. The Needleman-Wunsch algorithm performs a global alignment on two protein sequences or nucleotide sequences. It was the first application of dynamic programming to biological sequence comparison.<br /><br /><br />
<br />
When dynamic programming is applicable, the method takes far less time than naive methods. Using a naive method, many of the subproblems are generated and sovled many times. The dynamic programming approach seeks to solve each subproblem only once. Once the solution to a given subproblem has been computed, it is stored to be looked up next time.<br /><br /><br />
<br />
Like the Needleman-Wunsch algorithm, of which it is a variation, Smith-Waterman is also a dynamic programming algorithm. But it is a local sequence alignment algorithm. The famous BLAST(Basic Local Alignment Search Tool) is improved from Smith-Waterman algorithm. Although local algorithm has the desirable property that it is guaranteed to find the optimal local alignment, we decided to choose the global one because we regarded the segment sequence as a unit.<br /><br /><br />
<br />
Sequences are aligned with different detailed methods in different situations. In the regulated side, what we care about is the DNA sequence. In the regulating side, it is the amino acid sequence. When it comes to predict the regulated behavior, we use a DNA substitution matrix to align promoter and protein coding sequences. In the prediction of regulating behavior, the substitution matrix BLOSUM_50 is used to align the amino acid sequences translated from protein coding sequences.<br /><br /><br />
<br />
The promoter similarities of the query unit and subject units are stored in a vector. The protein coding similarities are stored in another vector. These vectors are prepared to be used in the new network construction.<br />
</p> <br />
</div> <br />
<br />
</div><br />
<br />
<br />
<h2>Forward Analysis</h2><br />
<div class="jobs_trigger"><strong>Construct New GRN</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<h3>User Input</h3><br />
<p align="justify"><br />
Some genes' regulation could be get from experiment. So, if users could get the unknow regulation between new gene and old ones, they could manually set the interactions which do not need model. Those regulations will be used in later simulation.<br />
</p><br />
<h3>Simalarity Analysis</h3><br />
<p align="justify"><b>1.Sequence</b></br><br />
<h4>Needleman-Wunsch Algorithm</h4><br />
The Needleman-Wunsch algorithm was first published in1970 by Saul B. Needleman and Christian D. Wunsch. It performs a global alignment of two sequences and is mostly used in bioinformatics to align protein or nucleotide sequence. Our software applied this algorithm in the alignment of DNA and amino acid sequences.<br/><br/><br />
<br />
The Needleman-Wunsch algorithm is one kind of dynamic programming and It was the first attempt in biological sequence comparison of dynamic programming.<br/><br/><br />
<br />
Here is an example of Needleman-Wunsch algorithm. S(a,b) is the similarity of character a and character b. The scores of characters are shown in the similarity matrix. We assume this matrix was<br />
</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/5/52/USTC_Software_DNA_S_M.png"/></div> <br />
<p>And we uses linear gap penalty, denoted by d, here, we set the gap penalty as -5.Then the alignment:</p><br />
<p align="center"><strong><em><br />
A: AGACTAGTTAC<br/><br />
B: CGA - - - GACGT<br />
</em></strong></p><br />
<br />
<p>would have the following score:</p><br />
<p align="center"><strong><em><br />
S(A,C)+S(G,C)+S(A,A)+(3)+S(G,G)+S(T,A)+S(T,C)+S(A,G)+S(C,T) = -3+7+10-(3x5)+7+(-4)+0+(-1)+0 = 1<br />
</em></strong></p><br />
<br />
<p align="justify">To find the highest score of alignment, in this algorithm, a two dimensional matrix F with sequences and scores was allocated. The score in row i, column j is denoted by Fij. There is one column for each character in sequence A and one row for each character in sequence B. Therefore, if we align sequences with sizes of n and m, the amount of memory taken up here is O(n,m).<br/><br/><br />
<br />
As the algorithm going on, Fij was calculated to be the optimal score by the principle as following:<br/><br />
Basis:<br />
</p><br />
<p align="center"><strong><em>Fi0 = d*i<br/>F0j = d*j</em></strong></p><br />
<p>Recursion:</p><br />
<p align="center"><strong><em>Fij = max(F(i-1,j-1) + S(Ai,Bj), F(i-1,j) + d, F(i,j-1) + d)</em></strong></p><br/><br />
<p>The pseudo-code of this algorithm would look like this:</p><br />
<br/><br />
<div id="pseudo"><p><br />
<strong> for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; F(i,0) <-- d*i<br/><br />
<strong> for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; F(0,j) <-- d*j<br/><br />
<strong>for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; <strong>for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; {<br/><br />
&nbsp; &nbsp; Match <-- F(i-1,j-1) + S(Ai,Bj)<br/><br />
&nbsp; &nbsp; Delete <-- F(i-1,j) + d<br/><br />
&nbsp; &nbsp; Insert <-- F(i,j-1) + d<br/><br />
&nbsp; &nbsp; F(i,j) <-- <strong>max</strong>(Match, Insert, Delete)<br/><br />
&nbsp; }<br />
</p><br />
</div><br />
<br />
<p align="justify">After the matrix F was computed, Fnm would be the maximum score among all possible alignment.<br/><br/><br />
<br />
If you want to see the optimal alignment, you can trace back from Fnm by comparing three possible sources mentioned in the above code (Match, Insert and Delete). If Match, then Aj and Bi are aligned, if Insert, Bi was aligned with a gap and if Delete, then Aj and a gap are aligned. Also, you may find there are not only one optimal alignment.<br/><br/><br />
As for the example, we would get the following matrix by applying Needleman Wunsch algorithm:</p><br />
<br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/e/e2/USTC_Software_arrow_game.png"/></div><br />
<p>And the optimal alignment would be:</p><br />
<br />
<p align="center"><strong><em>- - AGACTAGTTAC <br/><br />
CGAGAC - - GT - - -<br />
</em></strong></p><br />
<h4>A Supplementary Game</h4><br />
<p align="justify">The rows and columns in the GRN matrix can be regarded as vectors containing the regulated or the regulating information. The behavior similarity of two units can be described by the dot product of two regulated vectors or two regulating vectors. Biologists usually think the more similar two sequences are, the more likely they have similar behaviors. Whether the ratio of genes with similar behaviors is positively correlated with gene similarity is essential to our project. So we obtained 1.6 million sets of data by pairwise alignment of all the 1748 units in the GRN of K-12. Each set of data consists of gene similarity and behavior similarity. The result is analyzed and plotted in the figure. The linear fit shows that the ratio is positively correlated with the similarity.</p><br/><br />
<br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/2013/d/d0/USTC_Software_Simi-Ratio.png" /><br />
<p><strong>Figure 4.</strong>Linear fit of ratio-similarity relationship.</p></div><br />
<p align="justify">Although there are examples that a slight change in DNA sequence will significantly change the property of the gene, for example, sickle-cell disease, the influence is usually determined by the location and scale of the mutation. So the result is still convincing to some degree.</p><br />
<p><br />
<b>2.Filtering</b></p><br />
<h4>Random Noise</h4><br />
<p class="bodytext"></p><p align="justify">Normally, the similarity of two sequences will not be zero. Some computational<br />
experiments were carried out to study the random sequence similarities. We randomly<br />
chose a gene in the network and generated 1000 random sequences. The alignment result<br />
indicates that the random sequence similarities are Gauss distributed. The result suggests<br />
that some similarities are out of statistic significance.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/8/89/USTC_Software_Figure_4.png" /><br />
<p><strong>Figure 5.</strong> Random similarity distribution</p></div><br />
<h4>Filter</h4><br />
<p align="justify">We need the genes highly similar to the exogenous one to interact with it. The program will<br />
align the exogenous gene(query) with genes in the network(subject) and get the original<br />
similarities. In order to filter meaningless low values, a certain amount of random<br />
sequences are generated for each query-subject alignment. Normally, 100 is sufficient.<br />
Because the sequence length will influence alignment result, random sequences are fixed<br />
at the same length as the query one. Then align random sequences with the subject<br />
sequence. The statistic result of these random similarities is used as a threshold.<br /><br />
<div align="center">Threshold = μ + xσ</div><br /><br />
In the formula, μ is the average random similarity. σ is the standard deviation. x is used to<br />
control the filter determined by machine learning. If the original similarity is lower than the<br />
threshold, it is abandoned. It is usually means the original value is usually short of<br />
statistical significance.<br /><br /><br />
An example about filtring and consistency is presented in “Example”.<br />
</p><br />
<p><b>3.Regulation Calculation</b></p><br />
<p align="justify">If there is a three-unit network and they interact with each other as it is shown in the figure.<br />
The regulation is described by the GRN matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/8/8a/USTC_Software_Figure_5.png" /><br />
<p align="justify"><strong>Figure 6.</strong> Example network and its GRN matrix.</p></div><br />
<br />
<br />
<p align="justify">If D is the exogenous unit, we can obtain three similarity data sets of D with the units in the<br />
original GRN: <br />
<li style="margin-left:40px;">Promoter sequence similarity</li><br />
<li style="margin-left:40px;">Gene sequence similarity</li><br />
<li style="margin-left:40px;">Amino acid sequence similarity.</li><br />
<p><br />
The construction is equivalent to add a new column and a row into the original matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/9/97/USTC_Software_Figure_6.png" /><br />
<p><strong>Figure 7.</strong> Mathematical Equivalence</p></div><br />
<p align="justify">When filling the column, D is compared with the regulators of the unit in each row. The<br />
regulations in the row are consider separately and marked as “positive group” and<br />
“negative group”. The average similarity of each group represents the distance between<br />
the exogenous unit and the group. D is supposed to have the larger one's regulatory<br />
direction(positive or negative). The regulatory intensity is the weight average regulation of<br />
the chose group. The weight here is the amino acid sequence similarity.<br /><br /><br />
There are two conditions when fill the new row:<br /><br />
1. There are units having the same promoter as the exogenous unit.<br /><br />
2. There is no units having the same promoter as the exogenous unit.<br /><br /><br />
In condition 1, the units sharing the same promoter with the new member are picked out,<br />
and the following steps are the same as the construction of the column. The difference is<br />
the similarity used here is the gene sequence similarity. As explained in the regulation<br />
model part, the promoter is the main regulatory region, but the following sequence is also<br />
considered. Now the promoter is the same, so what we focus on are the gene sequences.<br /><br /><br />
In condition 2, the process is almost the same as constructing the new column. Promoter<br />
similarity is used because it is the main region.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/c/c5/USTC_Software_Figure_7.png" /><br />
</div><br />
<p><strong>Figure 8.</strong> Construct New GRN</p><br />
<h3>Clustering</h3><br />
<p><br />
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.</br></br><br />
For get a better regulation, we use online database DAVID to cluster all the genes in our whole GRN. Avoid of supersoftless, we hope to create an online communication with DAVID. After getting the cluster of our genes, we multiply the genes simalarity with a factor if they are in the same cluster.</br></br><br />
Though the source code of this part has already done, we lack the experiment information to set a propriate factor. All source code were pushed up to our github.<br />
</p><br />
</div><br />
<div class="jobs_trigger"><strong>Network Model</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Network analysis includes finding stable condition of network, adding new gene, finding new stable condition and changes from original condition to new condition. We use densities of materials to describe network condition. If all material densities are time-invariant, we can say the network condition is stable.</p><br />
<p class="bodytext"></p><p align="justify">Regulation relationship in genetic network includes positive regulation, negative regulation, positive-or-negative regulation and no regulation. We store regulation relationship in matrix R. Rji means the unit in line j and row i. For the material of original network, Rji=1 means material i enhance material j, Rji=-1 means material i repress material j, Rji=0 means material i has no influence on material j, Rji=2 means material i enhance or repress material j. For the new material, Rji ranges from -1 to 1. Rji<0 means the possibility of positive regulation is Rji; Rji>0 means the possibility of negative regulation is –Rji; Rji=0 means there is no regulation from i to j.<br />
We use Hill equations to describe intensity of regulation. Equations are like following:<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e0/USTC_Software_1.png" style="width:600px;"/><br />
<br/></br><br />
The left side of the equation is the derivative x(density) on t(time).”qi”,”pi”,”ri”,”mi”,”ni” are parameters, which determine the intensity of regulation."ri" is degradation rate. Mji is exponent. M is a matrix whose dimensions are equivalent to R's. Mji is 0 or ranges from 0.5 to 1.2 or ranges from -1.2 to 0.5. For the material of original network, if Rji=1,Mji ranges from 0.5 to 1.2;if Rji=-1, Mji ranges from -1.2 to -0.5; if Rji=2;Mji ranges from -1.2 to 0.5 or 0.5 to 1. These Mjis' absolute values are given randomly by program. If Rji=0, Mji=0. <br />
</br>For the new material,<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/64/USTC_Software_2.png"/><br />
<br/></br><br />
<br />
</p><br />
<p align="justify"><br />
Stable condition is the condition in which densities are time-invariant. We store material densities in a vector and solve the differential equations with Euler's formula, which is like below<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e6/USTC_Software_3.png" style="width:600px;"/><br />
<br/></br><br />
We know the network will be stable at last, so every material density has a limitation.<br />
<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Evaluate Network</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Record the original stable condition, set new material density to 0 and this is the new initial density vector. Solve new equations and record density vectors before the new condition is stable and store these data in a text file.</br></br><br />
<br />
To evaluate the new network, we introduce the grading system.<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/3/32/USTC_Software_4.png" style="width:600px;"/><br />
<img src="https://static.igem.org/mediawiki/2013/b/bc/USTC_Software_5.png" style="width:500px;"/><br />
<br />
<br/></br><br />
"xi" and "Xi" are densities of material i, which is not the new material."ny" is the number of materials. The more new densities are close to the original, the less the influence the cell endues. In general, cells close to the original cell are more likely to survive than those who are far different from the original cell. That is the thought of the grading system.</br></br><br />
We did a lot of running and found that the “AbsValue” ranges from 0 to 370, so "ScoreA" ranges from 0 to 4.9.We get the integer part and store it in an array, which has five sections. Generate 100 or 200 matrix M from matrix R and run the original and new network for each M, so we can get 100 or 200 of "ScoreA"s. The section which has maximum "ScoreA"s is the eventual score.<br />
</p> <br />
</div><br />
<br />
<br />
<h2>Reverse Analysis</h2><br />
<div class="jobs_trigger"><strong>Virtual Gene</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Before reverse analysis, we use the same idea about constructing a new GRN. So we create a virtual gene which replace the gene what users want to get. In calculation, it means that we add a row and a column to the matrix of GRN.</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Expression Range</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Before prediction, the expression of specific genes which the experimenter needs should be input into our software as well as the improvement or depression. The number of target gene is SIX at most.</br></br><br />
It is a must that figuring out the strongest and weakest expression strength before inputting the extreme cases into the target expression. The way to find out the strongest and weakest expression is modeling the GRN's steady state by a large amount of random regulation from -1 and 1. We ran it for 1000 times to get the range of gene expression. On the other hand, the expression of genes unpicked by the users should be stable as much as possible. The initial strength of expression is calculated by modeling the original GRN with Hill's equation.<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Particle Swarm Optimaztion</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify"><br />
For getting the best regulation, our software uses PSO algorithm based on 30 particles to simulate the GRN's changing. First of all, the interactions of regulator and regulated-by have been put into those particles in random so that each particle will have the whole set of regulation. Secondly, the variance between target expressions and stable expression of new GRN have been regarded as the optimize requirements in PSO algorithm. As a result, the minimal variance of 30 particles is the global optimum and the minimal variance of the procession in one particle is the local optimum. Then, taking a step towards global and local optimum as well as considering the inertia and perturbation avoids falling into the sub-optimal condition.</br></br><br />
At last, when the variance of expression reaches an acceptable range, our software picks out and saves the best global optimum particle following by the movement of those particles stop.</br></br><br />
We constantly revises the factors in PSO algorithm by machine learning method for accurate simulation with a fast PSO particle-motion equation. At the same time, our software also filter the result of regulatory value which is more intuitive.<br />
</p><br />
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<div class="jobs_trigger"><strong>Locate Optimal Target</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">To improve the efficiency of choosing a suitable gene after getting a series of regulatory value, our software picks out some obvious regulation. The value of regulation is between -1 to 1 in which -1 means negative effect and 1 means positive effect. As a result, what our software has done is filtering out the absolute value which is lower than 0.9. Because the difference of regulatory intensity lower than 0.1 has very little effect to the stable expression, the final result of regulation is indicated by Boolean value.</br></br><br />
The format of regulatory prediction in “Result”:</br><br />
Gene_name->Gene_name regulation(+/-)<br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/kuntestTeam:USTC-Software/kuntest2013-10-27T14:06:16Z<p>USTCkun: </p>
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<h1 align="justify">Methodologies</h1><br />
<p align="justify">In order to simulate the GRN's working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</br><br/><br />
There are four parts of methodologies: Database, Operon Theory and Regulatory Model, Forward Analysis and Reverse Analysis.<br />
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<h2>Database</h2><br />
<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Abstract</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">To simulate and analyze a genetic regulatory network (GRN), we need to build an objects' array to store the complete information of each gene. It contains regulation relationships between genes, sequences of genes, sequences of promoters and so on. However, it's hard to find an appropriate database online containing all information we need in a simple file. RegulonDB has downloadable files about the regulation between transcription factors (TF) and genes. Files about genetic information, transcription unit information and promoter information can also be downloaded from the RegulonDB. All those files have been put into file “source data” in the root directory of our software. They contain all information the simulation needs and we use fetching module to achieve data extraction and integration. There are four steps: fetch regulation relationships, fetch gene information, fetch promoter information and integrate information above.<br />
</p><br />
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<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Fetch Regulation</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">In GRN, there are two kinds of files: <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_tf.txt"> TF to TF</a> and <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_gene.txt">TF to Gene</a>. Since the database about the regulation between TFs and Genes contains only one-way interaction, the matrix of GRN is a rectangle.</br></br><br />
First of all, read the regulation relationship of TFs. Our software filters the documentation of RegulonDB on the head of all files and then reads the name of regulate and regulated TF, which is also the name of its genes, one by one. In the same time, our software numerates the genes and stores their names into an objects' array of genetic data. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/69/USTC_Software_TT.jpg"/><br />
The regulation of TFs has been put into a square matrix whose row is the regulator and column is the one regulated by. To make our GRN as complete as possible, the regulation between TF and genes has joined into the matrix. The one-way interaction results that we must read the TF in order to fulfill the regulator before completing the TF to gene's regulation in the same way of TF to TF. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/47/USTC_Software_TG.jpg"/><br />
At last, a regulatory matrix whose row represents regulate gene (TF) and whose column represents gene regulated by (TF+Gene) has been output into a file called “old_GRN” in root directory. The values in GRN matrix are regulations in which “1” means positive activation, “-1” means repression and “0” means no relationship. There have been some regulations both positive and negative identified regulations are determined by the experimental environment. As a result, our software picks out those uncertain genes and stores them into a file named “uncertain_database”.</br></br><br />
&nbsp;&nbsp;The format of uncertain database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;? &nbsp;&nbsp;&nbsp;Gene_name->Gene_name</br></br><br />
<br />
The question mark represents the unknown regulation between regulator and regulated-by whose names presented afterward. Users could replace the question mark with the data known in past experiment. (“+” rep positive, “-” rep negative). Our software will replace the values in matrix automatically. But if not rewrote, our software will regard those regulation as unknown.<br />
</p><br />
</div><br />
<br />
<div class="jobs_trigger"><strong> Fetch Gene Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify"><br />
All gene information has been deposited into a file named gene_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/Gene_sequence.txt">here</a>. In order of picking out the genes in GRN as fast as possible, all genetic information are stored in a “map”. “Map” is just like a dictionary yet its words are names of genes and its descriptions of words are replaced by genetic information. By using binary tree method, it is very fast to search the “word” wanted in the “dictionary”. As tested, the speed of binary tree method built-in “map” function is 720 times faster than traversal method.</br></br><br />
&nbsp;&nbsp;The format of Gene Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;Left_end_position &nbsp;&nbsp;&nbsp;Right_end_position &nbsp;&nbsp;&nbsp;DNA_strand &nbsp;&nbsp;&nbsp;Product_type &nbsp;&nbsp;&nbsp;&nbsp;Product_name &nbsp;&nbsp;&nbsp;Start_codon_sequence&nbsp;&nbsp;&nbsp; Stop_codon_sequence &nbsp;&nbsp;&nbsp;Gene_sequence</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/45/USTC_Software_GI.jpg"/><br />
The label of the map vector is gene name which will be picked out based on the names read in regulation matrix before. It is really fast using the binary tree method to find the specific genetic information and store them into a specific object. Those information includes gene ID, left position, right position, gene description and gene sequence. The gene ID is used to link to RegulonDB's gene details; The left position is used to find its specific transcription unit; The right position is used to figure out the base amount; The description of genes is used to distinguish the RNA and protein; The sequence is used to predict the regulation by alignment.<br />
<br />
</p><br />
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<br />
<br />
<div class="jobs_trigger"> <strong>Fetch Promoter Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">All promoter information has been deposited into a file named promoter_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/PromoterSet.txt">here</a>. But we also need transcription unit information because the information files about promoter do not contain all genes' names backward. “TU Info” file, which can be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/TUSet.txt">here</a>, contains the starting position of each TU and its promoter name. Our software picks out the starting position into a integer array. Using the left position picked out in gene info, our software would find out which unit the gene belongs to through dichotomy method and then stores the name of promoter into corresponding object.</br></br><br />
&nbsp;&nbsp;The format of TU info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Operon_name &nbsp;&nbsp;&nbsp;Unit_name &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;Transcription_start_site ......</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/1/1e/USTC_Software_TI.jpg"/><br />
The principle of fetching information of promoters is same as fetching genes's. Our software stores the promoter information from the file named “promoter_info” in a “map” which could be used to pick out the promoter sequence by searching promoter name through binary tree method.</br></br><br />
&nbsp;&nbsp;The format of Promoter Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Promoter_ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Promoter_name</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/8/8a/USTC_Software_PI.jpg"/><br />
The sequence of promoter will be used in the alignment method in next module which could make a prediction of exogenous genes' regulation pattern.<br />
</p> </div> <br />
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<br />
<br />
<div class="jobs_trigger"> <strong>Integration</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify"> <br />
Our software integrates all information we picked out about genes and generates a file named “all_info” —— all information about genes —— for the output graphical interface's reading. In the meanwhile, the array of objects containing all information has been stored in computer memory which greatly improve the computing speed of our software.</br></br><br />
&nbsp;&nbsp;The format of all_info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;No. &nbsp;&nbsp;&nbsp;promoter_sequence &nbsp;&nbsp;&nbsp;gene_sequence &nbsp;&nbsp;&nbsp;gene_name &nbsp;&nbsp;&nbsp;ID &nbsp;&nbsp;&nbsp;left_position &nbsp;&nbsp;&nbsp;right_position &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;&nbsp;description</br><br />
<br />
The fetching module generates three files: old_GRN, all_info and uncertain_database.</br><br />
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<div id="Alignment_Analyze"><br />
<h2>Operon Theory and Regulatory Model</h2><br />
<br />
<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Operon Theory</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><br />
<p align="justify">In genetics, an operon is a functioning unit of genomic DNA containing a cluster of genes<br />
under the control of a single regulatory signal or promoter. The genes contained in the<br />
operon are either expressed together or not at all. Several genes must be both cotranscribed<br />
and co-regulated to define an operon.<br /><br /><br />
The first time "operon" was proposed is in a paper of French Academic Science, 1960.<br />
The lac operon of the model bacterium E. coli was discovered and provides a typical<br />
example of operon function. It consists a promoter, an operator, three structural genes and<br />
a terminator. The operon is regulated by several factors including the availability of glucose<br />
and lactose.<br /><br /><br />
From this paper, the so-called general theory of the operon was developed. According to<br />
the theory, all genes are controlled by means of operons through a single feedback<br />
regulatory mechanism-repression. The first operon to be described was the lac operon in<br />
E. coli. The 1965 Nobel Prize in Physiology and Medicine was awarded to François Jacob,<br />
André Michel Lwoff and Jacques Lucien Monod for their discoveries concerning the operon and virus synthesis.<br /><br />
</p><br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/7/7d/USTC_Software_Figure_1.png" /><br />
<p align="center"><strong>Figure 1.</strong> Structure of Operon</p></div><br />
<p align="justify">An operon is made up of several structural genes arranged under a common promoter and<br />
regulated by a common operator. It is defined as a set of adjacent structural genes, plus<br />
the adjacent regulatory signals that affect transcription of the structural genes. The<br />
regulators of a given operon, including repressors, corepressors and activators, are not<br />
necessarily coded for by that operon.<br /><br /><br />
As a unit of transcription, upstream of the structural genes lies a promoter sequence which<br />
provides a site for RNA polymerase to bind and initiate transcription. Close to the promoter<br />
lies a section of DNA called an operator.<br /><br /><br />
Operon regulation can be either negative or positive by induction or repression. Negative<br />
control involves the binding of a repressor to the operator to prevent transcription.<br />
Operons can also be positively controlled. An activator protein binds to DNA, usually at a<br />
site other than the operator, to stimulate transcription.<br />
</p><br />
<div align="center"><img style="width:600px;" src="https://static.igem.org/mediawiki/igem.org/2/25/USTC_Software_Figure_2.png"/><br />
<p align="justify"><strong>Figure 2.</strong> Regulation of Operon<br />
1: RNA Polymerase, 2: Repressor, 3: Promoter, 4: Operator, 5: Lactose, 6: lacZ, 7:<br />
lacY, 8: lacA. Top: The gene is essentially turned off. There is no lactose to inhibit the<br />
repressor, so the repressor binds to the operator, which obstructs the RNA polymerase<br />
from binding to the promoter and making lactase.Bottom: The gene is turned on.Lactose<br />
is inhibiting the repressor, allowing the RNA polymerase to bind with the promoter, and<br />
express the genes, which synthesize lactase. Eventually, the lactase will digest all of the<br />
lactose, until there is none to bind to the repressor. The repressor will then bind to the<br />
operator, stopping the manufacture of lactase.</p></div><br />
<br />
<br />
</div><br />
<br />
<div class="jobs_trigger"><strong>Regulatory Model</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">Regulation of gene expression includes four levels. We choose the transcriptional level to simulate the regulation both for its significance and model simplification.</p><br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/igem.org/8/87/USTC_Software_Figure_3.png" /><br />
<p><strong>Figure 3.</strong>Regulation of gene expression.<br />Our regulation model is built based on the operon theory.<br /> The promoter region is regarded as the main regulatory region.</p></div><br />
</div><br />
<br />
<br />
<br />
<div class="jobs_trigger"> <strong>Similarity and Homology</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment. It is defined as the proportion of the common subsequence in the aligned sequence. Any two sequences share a certain<br />
similarity. It should be noted that similarity and homology are two different concepts.<br /><br /><br />
As with anatomical structures, homology between protein or DNA sequences is defined in<br />
terms of shared ancestry. Two segments of DNA can have shared ancestry because of<br />
either a speciation event or a duplication event. The terms “percent homology” and<br />
“sequence similarity” are often used interchangeably. As with anatomical structures, high<br />
sequence similarity might occur because of convergent evolution, or, as with shorter<br />
sequences, because of chance. Such sequences are similar but not homologous.<br />
Sequence regions that homologous are also called conserved.<br /><br /><br />
In our project, we use similarity to connect the exogenous gene with the original network.<br />
Because there is a good chance that the exogenous gene is not homologous with the<br />
genes in the network.</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The GRN matrix is the mathematical description of gene regulatory network in which “1” represents “enhance”, “-1” represents “repress” and “0” represents “no regulatory relationship”. The units(RU) in x-axis regulate the units in y-axis. A row can be seen as a vector containing all the information of the target(corresponding unit in the y-axis). Similarly, a column can be seen as a vector containing all the information of the regulator(corresponding unit in the x-axis).</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment based on Needleman-Wunsch algorithm[FIXME: wiki link here]. The Needleman-Wunsch algorithm performs a global alignment on two protein sequences or nucleotide sequences. It was the first application of dynamic programming to biological sequence comparison.<br /><br /><br />
<br />
When dynamic programming is applicable, the method takes far less time than naive methods. Using a naive method, many of the subproblems are generated and sovled many times. The dynamic programming approach seeks to solve each subproblem only once. Once the solution to a given subproblem has been computed, it is stored to be looked up next time.<br /><br /><br />
<br />
Like the Needleman-Wunsch algorithm, of which it is a variation, Smith-Waterman is also a dynamic programming algorithm. But it is a local sequence alignment algorithm. The famous BLAST(Basic Local Alignment Search Tool) is improved from Smith-Waterman algorithm. Although local algorithm has the desirable property that it is guaranteed to find the optimal local alignment, we decided to choose the global one because we regarded the segment sequence as a unit.<br /><br /><br />
<br />
Sequences are aligned with different detailed methods in different situations. In the regulated side, what we care about is the DNA sequence. In the regulating side, it is the amino acid sequence. When it comes to predict the regulated behavior, we use a DNA substitution matrix to align promoter and protein coding sequences. In the prediction of regulating behavior, the substitution matrix BLOSUM_50 is used to align the amino acid sequences translated from protein coding sequences.<br /><br /><br />
<br />
The promoter similarities of the query unit and subject units are stored in a vector. The protein coding similarities are stored in another vector. These vectors are prepared to be used in the new network construction.<br />
</p> <br />
</div> <br />
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</div><br />
<br />
<br />
<h2>Forward Analysis</h2><br />
<div class="jobs_trigger"><strong>Construct New GRN</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<h3>User Input</h3><br />
<p align="justify"><br />
Some genes' regulation could be get from experiment. So, if users could get the unknow regulation between new gene and old ones, they could manually set the interactions which do not need model. Those regulations will be used in later simulation.<br />
</p><br />
<h3>Simalarity Analysis</h3><br />
<p align="justify"><b>1.Sequence</b></br><br />
<h4>Needleman-Wunsch Algorithm</h4><br />
The Needleman-Wunsch algorithm was first published in1970 by Saul B. Needleman and Christian D. Wunsch. It performs a global alignment of two sequences and is mostly used in bioinformatics to align protein or nucleotide sequence. Our software applied this algorithm in the alignment of DNA and amino acid sequences.<br/><br/><br />
<br />
The Needleman-Wunsch algorithm is one kind of dynamic programming and It was the first attempt in biological sequence comparison of dynamic programming.<br/><br/><br />
<br />
Here is an example of Needleman-Wunsch algorithm. S(a,b) is the similarity of character a and character b. The scores of characters are shown in the similarity matrix. We assume this matrix was<br />
</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/5/52/USTC_Software_DNA_S_M.png"/></div> <br />
<p>And we uses linear gap penalty, denoted by d, here, we set the gap penalty as -5.Then the alignment:</p><br />
<p align="center"><strong><em><br />
A: AGACTAGTTAC<br/><br />
B: CGA - - - GACGT<br />
</em></strong></p><br />
<br />
<p>would have the following score:</p><br />
<p align="center"><strong><em><br />
S(A,C)+S(G,C)+S(A,A)+(3)+S(G,G)+S(T,A)+S(T,C)+S(A,G)+S(C,T) = -3+7+10-(3x5)+7+(-4)+0+(-1)+0 = 1<br />
</em></strong></p><br />
<br />
<p align="justify">To find the highest score of alignment, in this algorithm, a two dimensional matrix F with sequences and scores was allocated. The score in row i, column j is denoted by Fij. There is one column for each character in sequence A and one row for each character in sequence B. Therefore, if we align sequences with sizes of n and m, the amount of memory taken up here is O(n,m).<br/><br/><br />
<br />
As the algorithm going on, Fij was calculated to be the optimal score by the principle as following:<br/><br />
Basis:<br />
</p><br />
<p align="center"><strong><em>Fi0 = d*i<br/>F0j = d*j</em></strong></p><br />
<p>Recursion:</p><br />
<p align="center"><strong><em>Fij = max(F(i-1,j-1) + S(Ai,Bj), F(i-1,j) + d, F(i,j-1) + d)</em></strong></p><br/><br />
<p>The pseudo-code of this algorithm would look like this:</p><br />
<br/><br />
<div id="pseudo"><p><br />
<strong> for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; F(i,0) <-- d*i<br/><br />
<strong> for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; F(0,j) <-- d*j<br/><br />
<strong>for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; <strong>for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; {<br/><br />
&nbsp; &nbsp; Match <-- F(i-1,j-1) + S(Ai,Bj)<br/><br />
&nbsp; &nbsp; Delete <-- F(i-1,j) + d<br/><br />
&nbsp; &nbsp; Insert <-- F(i,j-1) + d<br/><br />
&nbsp; &nbsp; F(i,j) <-- <strong>max</strong>(Match, Insert, Delete)<br/><br />
&nbsp; }<br />
</p><br />
</div><br />
<br />
<p align="justify">After the matrix F was computed, Fnm would be the maximum score among all possible alignment.<br/><br/><br />
<br />
If you want to see the optimal alignment, you can trace back from Fnm by comparing three possible sources mentioned in the above code (Match, Insert and Delete). If Match, then Aj and Bi are aligned, if Insert, Bi was aligned with a gap and if Delete, then Aj and a gap are aligned. Also, you may find there are not only one optimal alignment.<br/><br/><br />
As for the example, we would get the following matrix by applying Needleman Wunsch algorithm:</p><br />
<br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/e/e2/USTC_Software_arrow_game.png"/></div><br />
<p>And the optimal alignment would be:</p><br />
<br />
<p align="center"><strong><em>- - AGACTAGTTAC <br/><br />
CGAGAC - - GT - - -<br />
</em></strong></p><br />
<h4>A Supplementary Game</h4><br />
<p align="justify">The rows and columns in the GRN matrix can be regarded as vectors containing the regulated or the regulating information. The behavior similarity of two units can be described by the dot product of two regulated vectors or two regulating vectors. Biologists usually think the more similar two sequences are, the more likely they have similar behaviors. Whether the ratio of genes with similar behaviors is positively correlated with gene similarity is essential to our project. So we obtained 1.6 million sets of data by pairwise alignment of all the 1748 units in the GRN of K-12. Each set of data consists of gene similarity and behavior similarity. The result is analyzed and plotted in the figure. The linear fit shows that the ratio is positively correlated with the similarity.</p><br/><br />
<br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/2013/d/d0/USTC_Software_Simi-Ratio.png" /><br />
<p><strong>Figure 4.</strong>Linear fit of ratio-similarity relationship.</p></div><br />
<p align="justify">Although there are examples that a slight change in DNA sequence will significantly change the property of the gene, for example, sickle-cell disease, the influence is usually determined by the location and scale of the mutation. So the result is still convincing to some degree.</p><br />
<p><br />
<b>2.Filtering</b></p><br />
<h4>Random Noise</h4><br />
<p class="bodytext"></p><p align="justify">Normally, the similarity of two sequences will not be zero. Some computational<br />
experiments were carried out to study the random sequence similarities. We randomly<br />
chose a gene in the network and generated 1000 random sequences. The alignment result<br />
indicates that the random sequence similarities are Gauss distributed. The result suggests<br />
that some similarities are out of statistic significance.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/8/89/USTC_Software_Figure_4.png" /><br />
<p><strong>Figure 5.</strong> Random similarity distribution</p></div><br />
<h4>Filter</h4><br />
<p align="justify">We need the genes highly similar to the exogenous one to interact with it. The program will<br />
align the exogenous gene(query) with genes in the network(subject) and get the original<br />
similarities. In order to filter meaningless low values, a certain amount of random<br />
sequences are generated for each query-subject alignment. Normally, 100 is sufficient.<br />
Because the sequence length will influence alignment result, random sequences are fixed<br />
at the same length as the query one. Then align random sequences with the subject<br />
sequence. The statistic result of these random similarities is used as a threshold.<br /><br />
<div align="center">Threshold = μ + xσ</div><br /><br />
In the formula, μ is the average random similarity. σ is the standard deviation. x is used to<br />
control the filter determined by machine learning. If the original similarity is lower than the<br />
threshold, it is abandoned. It is usually means the original value is usually short of<br />
statistical significance.<br /><br /><br />
An example about filtring and consistency is presented in “Example”.<br />
</p><br />
<p><b>3.Regulation Calculation</b></p><br />
<p align="justify">If there is a three-unit network and they interact with each other as it is shown in the figure.<br />
The regulation is described by the GRN matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/8/8a/USTC_Software_Figure_5.png" /><br />
<p align="justify"><strong>Figure 6.</strong> Example network and its GRN matrix.</p></div><br />
<br />
<br />
<p align="justify">If D is the exogenous unit, we can obtain three similarity data sets of D with the units in the<br />
original GRN: <br />
<li style="margin-left:40px;">Promoter sequence similarity</li><br />
<li style="margin-left:40px;">Gene sequence similarity</li><br />
<li style="margin-left:40px;">Amino acid sequence similarity.</li><br />
<p><br />
The construction is equivalent to add a new column and a row into the original matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/9/97/USTC_Software_Figure_6.png" /><br />
<p><strong>Figure 7.</strong> Mathematical Equivalence</p></div><br />
<p align="justify">When filling the column, D is compared with the regulators of the unit in each row. The<br />
regulations in the row are consider separately and marked as “positive group” and<br />
“negative group”. The average similarity of each group represents the distance between<br />
the exogenous unit and the group. D is supposed to have the larger one's regulatory<br />
direction(positive or negative). The regulatory intensity is the weight average regulation of<br />
the chose group. The weight here is the amino acid sequence similarity.<br /><br /><br />
There are two conditions when fill the new row:<br /><br />
1. There are units having the same promoter as the exogenous unit.<br /><br />
2. There is no units having the same promoter as the exogenous unit.<br /><br /><br />
In condition 1, the units sharing the same promoter with the new member are picked out,<br />
and the following steps are the same as the construction of the column. The difference is<br />
the similarity used here is the gene sequence similarity. As explained in the regulation<br />
model part, the promoter is the main regulatory region, but the following sequence is also<br />
considered. Now the promoter is the same, so what we focus on are the gene sequences.<br /><br /><br />
In condition 2, the process is almost the same as constructing the new column. Promoter<br />
similarity is used because it is the main region.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/c/c5/USTC_Software_Figure_7.png" /><br />
</div><br />
<p><strong>Figure 8.</strong> Construct New GRN</p><br />
<h3>Clustering</h3><br />
<p><br />
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.</br></br><br />
For get a better regulation, we use online database DAVID to cluster all the genes in our whole GRN. Avoid of supersoftless, we hope to create an online communication with DAVID. After getting the cluster of our genes, we multiply the genes simalarity with a factor if they are in the same cluster.</br></br><br />
Though the source code of this part has already done, we lack the experiment information to set a propriate factor. All source code were pushed up to our github.<br />
</p><br />
</div><br />
<div class="jobs_trigger"><strong>Network Model</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Network analysis includes finding stable condition of network, adding new gene, finding new stable condition and changes from original condition to new condition. We use densities of materials to describe network condition. If all material densities are time-invariant, we can say the network condition is stable.</p><br />
<p class="bodytext"></p><p align="justify">Regulation relationship in genetic network includes positive regulation, negative regulation, positive-or-negative regulation and no regulation. We store regulation relationship in matrix R. Rji means the unit in line j and row i. For the material of original network, Rji=1 means material i enhance material j, Rji=-1 means material i repress material j, Rji=0 means material i has no influence on material j, Rji=2 means material i enhance or repress material j. For the new material, Rji ranges from -1 to 1. Rji<0 means the possibility of positive regulation is Rji; Rji>0 means the possibility of negative regulation is –Rji; Rji=0 means there is no regulation from i to j.<br />
We use Hill equations to describe intensity of regulation. Equations are like following:<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e0/USTC_Software_1.png" style="width:600px;"/><br />
<br/></br><br />
The left side of the equation is the derivative x(density) on t(time).”qi”,”pi”,”ri”,”mi”,”ni” are parameters, which determine the intensity of regulation."ri" is degradation rate. Mji is exponent. M is a matrix whose dimensions are equivalent to R's. Mji is 0 or ranges from 0.5 to 1.2 or ranges from -1.2 to 0.5. For the material of original network, if Rji=1,Mji ranges from 0.5 to 1.2;if Rji=-1, Mji ranges from -1.2 to -0.5; if Rji=2;Mji ranges from -1.2 to 0.5 or 0.5 to 1. These Mjis' absolute values are given randomly by program. If Rji=0, Mji=0. <br />
</br>For the new material,<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/64/USTC_Software_2.png"/><br />
<br/></br><br />
<br />
</p><br />
<p align="justify"><br />
Stable condition is the condition in which densities are time-invariant. We store material densities in a vector and solve the differential equations with Euler's formula, which is like below<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e6/USTC_Software_3.png" style="width:600px;"/><br />
<br/></br><br />
We know the network will be stable at last, so every material density has a limitation.<br />
<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Evaluate Network</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Record the original stable condition, set new material density to 0 and this is the new initial density vector. Solve new equations and record density vectors before the new condition is stable and store these data in a text file.</br></br><br />
<br />
To evaluate the new network, we introduce the grading system.<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/3/32/USTC_Software_4.png" style="width:600px;"/><br />
<img src="https://static.igem.org/mediawiki/2013/b/bc/USTC_Software_5.png" style="width:500px;"/><br />
<br />
<br/></br><br />
"xi" and "Xi" are densities of material i, which is not the new material."ny" is the number of materials. The more new densities are close to the original, the less the influence the cell endues. In general, cells close to the original cell are more likely to survive than those who are far different from the original cell. That is the thought of the grading system.</br></br><br />
We did a lot of running and found that the “AbsValue” ranges from 0 to 370, so "ScoreA" ranges from 0 to 4.9.We get the integer part and store it in an array, which has five sections. Generate 100 or 200 matrix M from matrix R and run the original and new network for each M, so we can get 100 or 200 of "ScoreA"s. The section which has maximum "ScoreA"s is the eventual score.<br />
</p> <br />
</div><br />
<br />
<br />
<h2>Reverse Analysis</h2><br />
<div class="jobs_trigger"><strong>Virtual Gene</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<br />
<br />
<br />
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</div><br />
<div class="jobs_trigger"><strong>Expression Range</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Before prediction, the expression of specific genes which the experimenter needs should be input into our software as well as the improvement or depression. The number of target gene is SIX at most.</br></br><br />
It is a must that figuring out the strongest and weakest expression strength before inputting the extreme cases into the target expression. The way to find out the strongest and weakest expression is modeling the GRN's steady state by a large amount of random regulation from -1 and 1. We ran it for 1000 times to get the range of gene expression. On the other hand, the expression of genes unpicked by the users should be stable as much as possible. The initial strength of expression is calculated by modeling the original GRN with Hill's equation.<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Particle Swarm Optimaztion</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify"><br />
For getting the best regulation, our software uses PSO algorithm based on 30 particles to simulate the GRN's changing. First of all, the interactions of regulator and regulated-by have been put into those particles in random so that each particle will have the whole set of regulation. Secondly, the variance between target expressions and stable expression of new GRN have been regarded as the optimize requirements in PSO algorithm. As a result, the minimal variance of 30 particles is the global optimum and the minimal variance of the procession in one particle is the local optimum. Then, taking a step towards global and local optimum as well as considering the inertia and perturbation avoids falling into the sub-optimal condition.</br></br><br />
At last, when the variance of expression reaches an acceptable range, our software picks out and saves the best global optimum particle following by the movement of those particles stop.</br></br><br />
We constantly revises the factors in PSO algorithm by machine learning method for accurate simulation with a fast PSO particle-motion equation. At the same time, our software also filter the result of regulatory value which is more intuitive.<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Locate Optimal Target</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">To improve the efficiency of choosing a suitable gene after getting a series of regulatory value, our software picks out some obvious regulation. The value of regulation is between -1 to 1 in which -1 means negative effect and 1 means positive effect. As a result, what our software has done is filtering out the absolute value which is lower than 0.9. Because the difference of regulatory intensity lower than 0.1 has very little effect to the stable expression, the final result of regulation is indicated by Boolean value.</br></br><br />
The format of regulatory prediction in “Result”:</br><br />
Gene_name->Gene_name regulation(+/-)<br />
<br />
</p> <br />
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</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/kuntestTeam:USTC-Software/kuntest2013-10-27T14:02:51Z<p>USTCkun: </p>
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<h1 align="justify">Methodologies</h1><br />
<p align="justify">In order to simulate the GRN's working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</br><br/><br />
There are four parts of methodologies: Database, Operon Theory and Regulatory Model, Forward Analysis and Reverse Analysis.<br />
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<h2>Database</h2><br />
<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Abstract</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">To simulate and analyze a genetic regulatory network (GRN), we need to build an objects' array to store the complete information of each gene. It contains regulation relationships between genes, sequences of genes, sequences of promoters and so on. However, it's hard to find an appropriate database online containing all information we need in a simple file. RegulonDB has downloadable files about the regulation between transcription factors (TF) and genes. Files about genetic information, transcription unit information and promoter information can also be downloaded from the RegulonDB. All those files have been put into file “source data” in the root directory of our software. They contain all information the simulation needs and we use fetching module to achieve data extraction and integration. There are four steps: fetch regulation relationships, fetch gene information, fetch promoter information and integrate information above.<br />
</p><br />
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<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Fetch Regulation</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">In GRN, there are two kinds of files: <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_tf.txt"> TF to TF</a> and <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_gene.txt">TF to Gene</a>. Since the database about the regulation between TFs and Genes contains only one-way interaction, the matrix of GRN is a rectangle.</br></br><br />
First of all, read the regulation relationship of TFs. Our software filters the documentation of RegulonDB on the head of all files and then reads the name of regulate and regulated TF, which is also the name of its genes, one by one. In the same time, our software numerates the genes and stores their names into an objects' array of genetic data. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/69/USTC_Software_TT.jpg"/><br />
The regulation of TFs has been put into a square matrix whose row is the regulator and column is the one regulated by. To make our GRN as complete as possible, the regulation between TF and genes has joined into the matrix. The one-way interaction results that we must read the TF in order to fulfill the regulator before completing the TF to gene's regulation in the same way of TF to TF. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/47/USTC_Software_TG.jpg"/><br />
At last, a regulatory matrix whose row represents regulate gene (TF) and whose column represents gene regulated by (TF+Gene) has been output into a file called “old_GRN” in root directory. The values in GRN matrix are regulations in which “1” means positive activation, “-1” means repression and “0” means no relationship. There have been some regulations both positive and negative identified regulations are determined by the experimental environment. As a result, our software picks out those uncertain genes and stores them into a file named “uncertain_database”.</br></br><br />
&nbsp;&nbsp;The format of uncertain database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;? &nbsp;&nbsp;&nbsp;Gene_name->Gene_name</br></br><br />
<br />
The question mark represents the unknown regulation between regulator and regulated-by whose names presented afterward. Users could replace the question mark with the data known in past experiment. (“+” rep positive, “-” rep negative). Our software will replace the values in matrix automatically. But if not rewrote, our software will regard those regulation as unknown.<br />
</p><br />
</div><br />
<br />
<div class="jobs_trigger"><strong> Fetch Gene Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify"><br />
All gene information has been deposited into a file named gene_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/Gene_sequence.txt">here</a>. In order of picking out the genes in GRN as fast as possible, all genetic information are stored in a “map”. “Map” is just like a dictionary yet its words are names of genes and its descriptions of words are replaced by genetic information. By using binary tree method, it is very fast to search the “word” wanted in the “dictionary”. As tested, the speed of binary tree method built-in “map” function is 720 times faster than traversal method.</br></br><br />
&nbsp;&nbsp;The format of Gene Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;Left_end_position &nbsp;&nbsp;&nbsp;Right_end_position &nbsp;&nbsp;&nbsp;DNA_strand &nbsp;&nbsp;&nbsp;Product_type &nbsp;&nbsp;&nbsp;&nbsp;Product_name &nbsp;&nbsp;&nbsp;Start_codon_sequence&nbsp;&nbsp;&nbsp; Stop_codon_sequence &nbsp;&nbsp;&nbsp;Gene_sequence</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/45/USTC_Software_GI.jpg"/><br />
The label of the map vector is gene name which will be picked out based on the names read in regulation matrix before. It is really fast using the binary tree method to find the specific genetic information and store them into a specific object. Those information includes gene ID, left position, right position, gene description and gene sequence. The gene ID is used to link to RegulonDB's gene details; The left position is used to find its specific transcription unit; The right position is used to figure out the base amount; The description of genes is used to distinguish the RNA and protein; The sequence is used to predict the regulation by alignment.<br />
<br />
</p><br />
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<br />
<br />
<div class="jobs_trigger"> <strong>Fetch Promoter Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">All promoter information has been deposited into a file named promoter_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/PromoterSet.txt">here</a>. But we also need transcription unit information because the information files about promoter do not contain all genes' names backward. “TU Info” file, which can be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/TUSet.txt">here</a>, contains the starting position of each TU and its promoter name. Our software picks out the starting position into a integer array. Using the left position picked out in gene info, our software would find out which unit the gene belongs to through dichotomy method and then stores the name of promoter into corresponding object.</br></br><br />
&nbsp;&nbsp;The format of TU info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Operon_name &nbsp;&nbsp;&nbsp;Unit_name &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;Transcription_start_site ......</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/1/1e/USTC_Software_TI.jpg"/><br />
The principle of fetching information of promoters is same as fetching genes's. Our software stores the promoter information from the file named “promoter_info” in a “map” which could be used to pick out the promoter sequence by searching promoter name through binary tree method.</br></br><br />
&nbsp;&nbsp;The format of Promoter Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Promoter_ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Promoter_name</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/8/8a/USTC_Software_PI.jpg"/><br />
The sequence of promoter will be used in the alignment method in next module which could make a prediction of exogenous genes' regulation pattern.<br />
</p> </div> <br />
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<br />
<br />
<div class="jobs_trigger"> <strong>Integration</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify"> <br />
Our software integrates all information we picked out about genes and generates a file named “all_info” —— all information about genes —— for the output graphical interface's reading. In the meanwhile, the array of objects containing all information has been stored in computer memory which greatly improve the computing speed of our software.</br></br><br />
&nbsp;&nbsp;The format of all_info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;No. &nbsp;&nbsp;&nbsp;promoter_sequence &nbsp;&nbsp;&nbsp;gene_sequence &nbsp;&nbsp;&nbsp;gene_name &nbsp;&nbsp;&nbsp;ID &nbsp;&nbsp;&nbsp;left_position &nbsp;&nbsp;&nbsp;right_position &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;&nbsp;description</br><br />
<br />
The fetching module generates three files: old_GRN, all_info and uncertain_database.</br><br />
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<h2>Operon Theory and Regulatory Model</h2><br />
<br />
<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Operon Theory</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><br />
<p align="justify">In genetics, an operon is a functioning unit of genomic DNA containing a cluster of genes<br />
under the control of a single regulatory signal or promoter. The genes contained in the<br />
operon are either expressed together or not at all. Several genes must be both cotranscribed<br />
and co-regulated to define an operon.<br /><br /><br />
The first time "operon" was proposed is in a paper of French Academic Science, 1960.<br />
The lac operon of the model bacterium E. coli was discovered and provides a typical<br />
example of operon function. It consists a promoter, an operator, three structural genes and<br />
a terminator. The operon is regulated by several factors including the availability of glucose<br />
and lactose.<br /><br /><br />
From this paper, the so-called general theory of the operon was developed. According to<br />
the theory, all genes are controlled by means of operons through a single feedback<br />
regulatory mechanism-repression. The first operon to be described was the lac operon in<br />
E. coli. The 1965 Nobel Prize in Physiology and Medicine was awarded to François Jacob,<br />
André Michel Lwoff and Jacques Lucien Monod for their discoveries concerning the operon and virus synthesis.<br /><br />
</p><br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/7/7d/USTC_Software_Figure_1.png" /><br />
<p align="center"><strong>Figure 1.</strong> Structure of Operon</p></div><br />
<p align="justify">An operon is made up of several structural genes arranged under a common promoter and<br />
regulated by a common operator. It is defined as a set of adjacent structural genes, plus<br />
the adjacent regulatory signals that affect transcription of the structural genes. The<br />
regulators of a given operon, including repressors, corepressors and activators, are not<br />
necessarily coded for by that operon.<br /><br /><br />
As a unit of transcription, upstream of the structural genes lies a promoter sequence which<br />
provides a site for RNA polymerase to bind and initiate transcription. Close to the promoter<br />
lies a section of DNA called an operator.<br /><br /><br />
Operon regulation can be either negative or positive by induction or repression. Negative<br />
control involves the binding of a repressor to the operator to prevent transcription.<br />
Operons can also be positively controlled. An activator protein binds to DNA, usually at a<br />
site other than the operator, to stimulate transcription.<br />
</p><br />
<div align="center"><img style="width:600px;" src="https://static.igem.org/mediawiki/igem.org/2/25/USTC_Software_Figure_2.png"/><br />
<p align="justify"><strong>Figure 2.</strong> Regulation of Operon<br />
1: RNA Polymerase, 2: Repressor, 3: Promoter, 4: Operator, 5: Lactose, 6: lacZ, 7:<br />
lacY, 8: lacA. Top: The gene is essentially turned off. There is no lactose to inhibit the<br />
repressor, so the repressor binds to the operator, which obstructs the RNA polymerase<br />
from binding to the promoter and making lactase.Bottom: The gene is turned on.Lactose<br />
is inhibiting the repressor, allowing the RNA polymerase to bind with the promoter, and<br />
express the genes, which synthesize lactase. Eventually, the lactase will digest all of the<br />
lactose, until there is none to bind to the repressor. The repressor will then bind to the<br />
operator, stopping the manufacture of lactase.</p></div><br />
<br />
<br />
</div><br />
<br />
<div class="jobs_trigger"><strong>Regulatory Model</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">Regulation of gene expression includes four levels. We choose the transcriptional level to simulate the regulation both for its significance and model simplification.</p><br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/igem.org/8/87/USTC_Software_Figure_3.png" /><br />
<p><strong>Figure 3.</strong>Regulation of gene expression.<br />Our regulation model is built based on the operon theory.<br /> The promoter region is regarded as the main regulatory region.</p></div><br />
</div><br />
<br />
<br />
<br />
<div class="jobs_trigger"> <strong>Similarity and Homology</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment. It is defined as the proportion of the common subsequence in the aligned sequence. Any two sequences share a certain<br />
similarity. It should be noted that similarity and homology are two different concepts.<br /><br /><br />
As with anatomical structures, homology between protein or DNA sequences is defined in<br />
terms of shared ancestry. Two segments of DNA can have shared ancestry because of<br />
either a speciation event or a duplication event. The terms “percent homology” and<br />
“sequence similarity” are often used interchangeably. As with anatomical structures, high<br />
sequence similarity might occur because of convergent evolution, or, as with shorter<br />
sequences, because of chance. Such sequences are similar but not homologous.<br />
Sequence regions that homologous are also called conserved.<br /><br /><br />
In our project, we use similarity to connect the exogenous gene with the original network.<br />
Because there is a good chance that the exogenous gene is not homologous with the<br />
genes in the network.</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The GRN matrix is the mathematical description of gene regulatory network in which “1” represents “enhance”, “-1” represents “repress” and “0” represents “no regulatory relationship”. The units(RU) in x-axis regulate the units in y-axis. A row can be seen as a vector containing all the information of the target(corresponding unit in the y-axis). Similarly, a column can be seen as a vector containing all the information of the regulator(corresponding unit in the x-axis).</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment based on Needleman-Wunsch algorithm[FIXME: wiki link here]. The Needleman-Wunsch algorithm performs a global alignment on two protein sequences or nucleotide sequences. It was the first application of dynamic programming to biological sequence comparison.<br /><br /><br />
<br />
When dynamic programming is applicable, the method takes far less time than naive methods. Using a naive method, many of the subproblems are generated and sovled many times. The dynamic programming approach seeks to solve each subproblem only once. Once the solution to a given subproblem has been computed, it is stored to be looked up next time.<br /><br /><br />
<br />
Like the Needleman-Wunsch algorithm, of which it is a variation, Smith-Waterman is also a dynamic programming algorithm. But it is a local sequence alignment algorithm. The famous BLAST(Basic Local Alignment Search Tool) is improved from Smith-Waterman algorithm. Although local algorithm has the desirable property that it is guaranteed to find the optimal local alignment, we decided to choose the global one because we regarded the segment sequence as a unit.<br /><br /><br />
<br />
Sequences are aligned with different detailed methods in different situations. In the regulated side, what we care about is the DNA sequence. In the regulating side, it is the amino acid sequence. When it comes to predict the regulated behavior, we use a DNA substitution matrix to align promoter and protein coding sequences. In the prediction of regulating behavior, the substitution matrix BLOSUM_50 is used to align the amino acid sequences translated from protein coding sequences.<br /><br /><br />
<br />
The promoter similarities of the query unit and subject units are stored in a vector. The protein coding similarities are stored in another vector. These vectors are prepared to be used in the new network construction.<br />
</p> <br />
</div> <br />
<br />
</div><br />
<br />
<br />
<h2>Forward Analysis</h2><br />
<div class="jobs_trigger"><strong>Construct New GRN</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<h3>User Input</h3><br />
<p align="justify"><br />
Some genes' regulation could be get from experiment. So, if users could get the unknow regulation between new gene and old ones, they could manually set the interactions which do not need model. Those regulations will be used in later simulation.<br />
</p><br />
<h3>Simalarity Analysis</h3><br />
<p align="justify"><b>1.Sequence</b></br><br />
<h4>Needleman-Wunsch Algorithm</h4><br />
The Needleman-Wunsch algorithm was first published in1970 by Saul B. Needleman and Christian D. Wunsch. It performs a global alignment of two sequences and is mostly used in bioinformatics to align protein or nucleotide sequence. Our software applied this algorithm in the alignment of DNA and amino acid sequences.<br/><br/><br />
<br />
The Needleman-Wunsch algorithm is one kind of dynamic programming and It was the first attempt in biological sequence comparison of dynamic programming.<br/><br/><br />
<br />
Here is an example of Needleman-Wunsch algorithm. S(a,b) is the similarity of character a and character b. The scores of characters are shown in the similarity matrix. We assume this matrix was<br />
</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/5/52/USTC_Software_DNA_S_M.png"/></div> <br />
<p>And we uses linear gap penalty, denoted by d, here, we set the gap penalty as -5.Then the alignment:</p><br />
<p align="center"><strong><em><br />
A: AGACTAGTTAC<br/><br />
B: CGA - - - GACGT<br />
</em></strong></p><br />
<br />
<p>would have the following score:</p><br />
<p align="center"><strong><em><br />
S(A,C)+S(G,C)+S(A,A)+(3)+S(G,G)+S(T,A)+S(T,C)+S(A,G)+S(C,T) = -3+7+10-(3x5)+7+(-4)+0+(-1)+0 = 1<br />
</em></strong></p><br />
<br />
<p align="justify">To find the highest score of alignment, in this algorithm, a two dimensional matrix F with sequences and scores was allocated. The score in row i, column j is denoted by Fij. There is one column for each character in sequence A and one row for each character in sequence B. Therefore, if we align sequences with sizes of n and m, the amount of memory taken up here is O(n,m).<br/><br/><br />
<br />
As the algorithm going on, Fij was calculated to be the optimal score by the principle as following:<br/><br />
Basis:<br />
</p><br />
<p align="center"><strong><em>Fi0 = d*i<br/>F0j = d*j</em></strong></p><br />
<p>Recursion:</p><br />
<p align="center"><strong><em>Fij = max(F(i-1,j-1) + S(Ai,Bj), F(i-1,j) + d, F(i,j-1) + d)</em></strong></p><br/><br />
<p>The pseudo-code of this algorithm would look like this:</p><br />
<br/><br />
<div id="pseudo"><p><br />
<strong> for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; F(i,0) <-- d*i<br/><br />
<strong> for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; F(0,j) <-- d*j<br/><br />
<strong>for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; <strong>for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; {<br/><br />
&nbsp; &nbsp; Match <-- F(i-1,j-1) + S(Ai,Bj)<br/><br />
&nbsp; &nbsp; Delete <-- F(i-1,j) + d<br/><br />
&nbsp; &nbsp; Insert <-- F(i,j-1) + d<br/><br />
&nbsp; &nbsp; F(i,j) <-- <strong>max</strong>(Match, Insert, Delete)<br/><br />
&nbsp; }<br />
</p><br />
</div><br />
<br />
<p align="justify">After the matrix F was computed, Fnm would be the maximum score among all possible alignment.<br/><br/><br />
<br />
If you want to see the optimal alignment, you can trace back from Fnm by comparing three possible sources mentioned in the above code (Match, Insert and Delete). If Match, then Aj and Bi are aligned, if Insert, Bi was aligned with a gap and if Delete, then Aj and a gap are aligned. Also, you may find there are not only one optimal alignment.<br/><br/><br />
As for the example, we would get the following matrix by applying Needleman Wunsch algorithm:</p><br />
<br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/e/e2/USTC_Software_arrow_game.png"/></div><br />
<p>And the optimal alignment would be:</p><br />
<br />
<p align="center"><strong><em>- - AGACTAGTTAC <br/><br />
CGAGAC - - GT - - -<br />
</em></strong></p><br />
<h4>A Supplementary Game</h4><br />
<p align="justify">The rows and columns in the GRN matrix can be regarded as vectors containing the regulated or the regulating information. The behavior similarity of two units can be described by the dot product of two regulated vectors or two regulating vectors. Biologists usually think the more similar two sequences are, the more likely they have similar behaviors. Whether the ratio of genes with similar behaviors is positively correlated with gene similarity is essential to our project. So we obtained 1.6 million sets of data by pairwise alignment of all the 1748 units in the GRN of K-12. Each set of data consists of gene similarity and behavior similarity. The result is analyzed and plotted in the figure. The linear fit shows that the ratio is positively correlated with the similarity.</p><br/><br />
<br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/2013/d/d0/USTC_Software_Simi-Ratio.png" /><br />
<p><strong>Figure 4.</strong>Linear fit of ratio-similarity relationship.</p></div><br />
<p align="justify">Although there are examples that a slight change in DNA sequence will significantly change the property of the gene, for example, sickle-cell disease, the influence is usually determined by the location and scale of the mutation. So the result is still convincing to some degree.</p><br />
<p><br />
<b>2.Filtering</b></p><br />
<h4>Random Noise</h4><br />
<p class="bodytext"></p><p align="justify">Normally, the similarity of two sequences will not be zero. Some computational<br />
experiments were carried out to study the random sequence similarities. We randomly<br />
chose a gene in the network and generated 1000 random sequences. The alignment result<br />
indicates that the random sequence similarities are Gauss distributed. The result suggests<br />
that some similarities are out of statistic significance.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/8/89/USTC_Software_Figure_4.png" /><br />
<p><strong>Figure 5.</strong> Random similarity distribution</p></div><br />
<h4>Filter</h4><br />
<p align="justify">We need the genes highly similar to the exogenous one to interact with it. The program will<br />
align the exogenous gene(query) with genes in the network(subject) and get the original<br />
similarities. In order to filter meaningless low values, a certain amount of random<br />
sequences are generated for each query-subject alignment. Normally, 100 is sufficient.<br />
Because the sequence length will influence alignment result, random sequences are fixed<br />
at the same length as the query one. Then align random sequences with the subject<br />
sequence. The statistic result of these random similarities is used as a threshold.<br /><br />
<div align="center">Threshold = μ + xσ</div><br /><br />
In the formula, μ is the average random similarity. σ is the standard deviation. x is used to<br />
control the filter determined by machine learning. If the original similarity is lower than the<br />
threshold, it is abandoned. It is usually means the original value is usually short of<br />
statistical significance.<br /><br /><br />
An example about filtring and consistency is presented in “Example”.<br />
</p><br />
<p><b>3.Regulation Calculation</b></p><br />
<p align="justify">If there is a three-unit network and they interact with each other as it is shown in the figure.<br />
The regulation is described by the GRN matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/8/8a/USTC_Software_Figure_5.png" /><br />
<p align="justify"><strong>Figure 6.</strong> Example network and its GRN matrix.</p></div><br />
<br />
<br />
<p align="justify">If D is the exogenous unit, we can obtain three similarity data sets of D with the units in the<br />
original GRN: <br />
<li style="margin-left:40px;">Promoter sequence similarity</li><br />
<li style="margin-left:40px;">Gene sequence similarity</li><br />
<li style="margin-left:40px;">Amino acid sequence similarity.</li><br />
<p><br />
The construction is equivalent to add a new column and a row into the original matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/9/97/USTC_Software_Figure_6.png" /><br />
<p><strong>Figure 7.</strong> Mathematical Equivalence</p></div><br />
<p align="justify">When filling the column, D is compared with the regulators of the unit in each row. The<br />
regulations in the row are consider separately and marked as “positive group” and<br />
“negative group”. The average similarity of each group represents the distance between<br />
the exogenous unit and the group. D is supposed to have the larger one's regulatory<br />
direction(positive or negative). The regulatory intensity is the weight average regulation of<br />
the chose group. The weight here is the amino acid sequence similarity.<br /><br /><br />
There are two conditions when fill the new row:<br /><br />
1. There are units having the same promoter as the exogenous unit.<br /><br />
2. There is no units having the same promoter as the exogenous unit.<br /><br /><br />
In condition 1, the units sharing the same promoter with the new member are picked out,<br />
and the following steps are the same as the construction of the column. The difference is<br />
the similarity used here is the gene sequence similarity. As explained in the regulation<br />
model part, the promoter is the main regulatory region, but the following sequence is also<br />
considered. Now the promoter is the same, so what we focus on are the gene sequences.<br /><br /><br />
In condition 2, the process is almost the same as constructing the new column. Promoter<br />
similarity is used because it is the main region.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/c/c5/USTC_Software_Figure_7.png" /><br />
<p><strong>Figure 8.</strong> Construct New GRN</p><br />
<h3>Clustering</h3><br />
<p><br />
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.</br></br><br />
For get a better regulation, we use online database DAVID to cluster all the genes in our whole GRN. Avoid of supersoftless, we hope to create an online communication with DAVID. After getting the cluster of our genes, we multiply the genes simalarity with a factor if they are in the same cluster.</br></br><br />
Though the source code of this part has already done, we lack the experiment information to set a propriate factor. All source code were pushed up to our github.<br />
</p><br />
</div><br />
</div><br />
<div class="jobs_trigger"><strong>Network Model</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Network analysis includes finding stable condition of network, adding new gene, finding new stable condition and changes from original condition to new condition. We use densities of materials to describe network condition. If all material densities are time-invariant, we can say the network condition is stable.</p><br />
<p class="bodytext"></p><p align="justify">Regulation relationship in genetic network includes positive regulation, negative regulation, positive-or-negative regulation and no regulation. We store regulation relationship in matrix R. Rji means the unit in line j and row i. For the material of original network, Rji=1 means material i enhance material j, Rji=-1 means material i repress material j, Rji=0 means material i has no influence on material j, Rji=2 means material i enhance or repress material j. For the new material, Rji ranges from -1 to 1. Rji<0 means the possibility of positive regulation is Rji; Rji>0 means the possibility of negative regulation is –Rji; Rji=0 means there is no regulation from i to j.<br />
We use Hill equations to describe intensity of regulation. Equations are like following:<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e0/USTC_Software_1.png" style="width:600px;"/><br />
<br/></br><br />
The left side of the equation is the derivative x(density) on t(time).”qi”,”pi”,”ri”,”mi”,”ni” are parameters, which determine the intensity of regulation."ri" is degradation rate. Mji is exponent. M is a matrix whose dimensions are equivalent to R's. Mji is 0 or ranges from 0.5 to 1.2 or ranges from -1.2 to 0.5. For the material of original network, if Rji=1,Mji ranges from 0.5 to 1.2;if Rji=-1, Mji ranges from -1.2 to -0.5; if Rji=2;Mji ranges from -1.2 to 0.5 or 0.5 to 1. These Mjis' absolute values are given randomly by program. If Rji=0, Mji=0. <br />
</br>For the new material,<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/64/USTC_Software_2.png"/><br />
<br/></br><br />
<br />
</p><br />
<p align="justify"><br />
Stable condition is the condition in which densities are time-invariant. We store material densities in a vector and solve the differential equations with Euler's formula, which is like below<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e6/USTC_Software_3.png" style="width:600px;"/><br />
<br/></br><br />
We know the network will be stable at last, so every material density has a limitation.<br />
<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Evaluate Network</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Record the original stable condition, set new material density to 0 and this is the new initial density vector. Solve new equations and record density vectors before the new condition is stable and store these data in a text file.</br></br><br />
<br />
To evaluate the new network, we introduce the grading system.<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/3/32/USTC_Software_4.png" style="width:600px;"/><br />
<img src="https://static.igem.org/mediawiki/2013/b/bc/USTC_Software_5.png" style="width:500px;"/><br />
<br />
<br/></br><br />
"xi" and "Xi" are densities of material i, which is not the new material."ny" is the number of materials. The more new densities are close to the original, the less the influence the cell endues. In general, cells close to the original cell are more likely to survive than those who are far different from the original cell. That is the thought of the grading system.</br></br><br />
We did a lot of running and found that the “AbsValue” ranges from 0 to 370, so "ScoreA" ranges from 0 to 4.9.We get the integer part and store it in an array, which has five sections. Generate 100 or 200 matrix M from matrix R and run the original and new network for each M, so we can get 100 or 200 of "ScoreA"s. The section which has maximum "ScoreA"s is the eventual score.<br />
</p> <br />
</div><br />
<br />
<br />
<h2>Reverse Analysis</h2><br />
<div class="jobs_trigger"><strong>Virtual Gene</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Expression Range</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Before prediction, the expression of specific genes which the experimenter needs should be input into our software as well as the improvement or depression. The number of target gene is SIX at most.</br></br><br />
It is a must that figuring out the strongest and weakest expression strength before inputting the extreme cases into the target expression. The way to find out the strongest and weakest expression is modeling the GRN's steady state by a large amount of random regulation from -1 and 1. We ran it for 1000 times to get the range of gene expression. On the other hand, the expression of genes unpicked by the users should be stable as much as possible. The initial strength of expression is calculated by modeling the original GRN with Hill's equation.<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Particle Swarm Optimaztion</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify"><br />
For getting the best regulation, our software uses PSO algorithm based on 30 particles to simulate the GRN's changing. First of all, the interactions of regulator and regulated-by have been put into those particles in random so that each particle will have the whole set of regulation. Secondly, the variance between target expressions and stable expression of new GRN have been regarded as the optimize requirements in PSO algorithm. As a result, the minimal variance of 30 particles is the global optimum and the minimal variance of the procession in one particle is the local optimum. Then, taking a step towards global and local optimum as well as considering the inertia and perturbation avoids falling into the sub-optimal condition.</br></br><br />
At last, when the variance of expression reaches an acceptable range, our software picks out and saves the best global optimum particle following by the movement of those particles stop.</br></br><br />
We constantly revises the factors in PSO algorithm by machine learning method for accurate simulation with a fast PSO particle-motion equation. At the same time, our software also filter the result of regulatory value which is more intuitive.<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Locate Optimal Target</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">To improve the efficiency of choosing a suitable gene after getting a series of regulatory value, our software picks out some obvious regulation. The value of regulation is between -1 to 1 in which -1 means negative effect and 1 means positive effect. As a result, what our software has done is filtering out the absolute value which is lower than 0.9. Because the difference of regulatory intensity lower than 0.1 has very little effect to the stable expression, the final result of regulation is indicated by Boolean value.</br></br><br />
The format of regulatory prediction in “Result”:</br><br />
Gene_name->Gene_name regulation(+/-)<br />
<br />
</p> <br />
<br />
<br />
<br />
</div><br />
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<div id="abstract"><br />
<h1 align="justify">Methodologies</h1><br />
<p align="justify">In order to simulate the GRN's working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</br><br/><br />
There are four parts of methodologies: Database, Operon Theory and Regulatory Model, Forward Analysis and Reverse Analysis.<br />
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<h2>Database</h2><br />
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<div class="jobs_trigger"><strong>Abstract</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">To simulate and analyze a genetic regulatory network (GRN), we need to build an objects' array to store the complete information of each gene. It contains regulation relationships between genes, sequences of genes, sequences of promoters and so on. However, it's hard to find an appropriate database online containing all information we need in a simple file. RegulonDB has downloadable files about the regulation between transcription factors (TF) and genes. Files about genetic information, transcription unit information and promoter information can also be downloaded from the RegulonDB. All those files have been put into file “source data” in the root directory of our software. They contain all information the simulation needs and we use fetching module to achieve data extraction and integration. There are four steps: fetch regulation relationships, fetch gene information, fetch promoter information and integrate information above.<br />
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<div class="jobs_trigger"><strong>Fetch Regulation</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">In GRN, there are two kinds of files: <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_tf.txt"> TF to TF</a> and <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_gene.txt">TF to Gene</a>. Since the database about the regulation between TFs and Genes contains only one-way interaction, the matrix of GRN is a rectangle.</br></br><br />
First of all, read the regulation relationship of TFs. Our software filters the documentation of RegulonDB on the head of all files and then reads the name of regulate and regulated TF, which is also the name of its genes, one by one. In the same time, our software numerates the genes and stores their names into an objects' array of genetic data. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/69/USTC_Software_TT.jpg"/><br />
The regulation of TFs has been put into a square matrix whose row is the regulator and column is the one regulated by. To make our GRN as complete as possible, the regulation between TF and genes has joined into the matrix. The one-way interaction results that we must read the TF in order to fulfill the regulator before completing the TF to gene's regulation in the same way of TF to TF. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/47/USTC_Software_TG.jpg"/><br />
At last, a regulatory matrix whose row represents regulate gene (TF) and whose column represents gene regulated by (TF+Gene) has been output into a file called “old_GRN” in root directory. The values in GRN matrix are regulations in which “1” means positive activation, “-1” means repression and “0” means no relationship. There have been some regulations both positive and negative identified regulations are determined by the experimental environment. As a result, our software picks out those uncertain genes and stores them into a file named “uncertain_database”.</br></br><br />
&nbsp;&nbsp;The format of uncertain database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;? &nbsp;&nbsp;&nbsp;Gene_name->Gene_name</br></br><br />
<br />
The question mark represents the unknown regulation between regulator and regulated-by whose names presented afterward. Users could replace the question mark with the data known in past experiment. (“+” rep positive, “-” rep negative). Our software will replace the values in matrix automatically. But if not rewrote, our software will regard those regulation as unknown.<br />
</p><br />
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<div class="jobs_trigger"><strong> Fetch Gene Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify"><br />
All gene information has been deposited into a file named gene_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/Gene_sequence.txt">here</a>. In order of picking out the genes in GRN as fast as possible, all genetic information are stored in a “map”. “Map” is just like a dictionary yet its words are names of genes and its descriptions of words are replaced by genetic information. By using binary tree method, it is very fast to search the “word” wanted in the “dictionary”. As tested, the speed of binary tree method built-in “map” function is 720 times faster than traversal method.</br></br><br />
&nbsp;&nbsp;The format of Gene Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;Left_end_position &nbsp;&nbsp;&nbsp;Right_end_position &nbsp;&nbsp;&nbsp;DNA_strand &nbsp;&nbsp;&nbsp;Product_type &nbsp;&nbsp;&nbsp;&nbsp;Product_name &nbsp;&nbsp;&nbsp;Start_codon_sequence&nbsp;&nbsp;&nbsp; Stop_codon_sequence &nbsp;&nbsp;&nbsp;Gene_sequence</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/45/USTC_Software_GI.jpg"/><br />
The label of the map vector is gene name which will be picked out based on the names read in regulation matrix before. It is really fast using the binary tree method to find the specific genetic information and store them into a specific object. Those information includes gene ID, left position, right position, gene description and gene sequence. The gene ID is used to link to RegulonDB's gene details; The left position is used to find its specific transcription unit; The right position is used to figure out the base amount; The description of genes is used to distinguish the RNA and protein; The sequence is used to predict the regulation by alignment.<br />
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<div class="jobs_trigger"> <strong>Fetch Promoter Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">All promoter information has been deposited into a file named promoter_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/PromoterSet.txt">here</a>. But we also need transcription unit information because the information files about promoter do not contain all genes' names backward. “TU Info” file, which can be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/TUSet.txt">here</a>, contains the starting position of each TU and its promoter name. Our software picks out the starting position into a integer array. Using the left position picked out in gene info, our software would find out which unit the gene belongs to through dichotomy method and then stores the name of promoter into corresponding object.</br></br><br />
&nbsp;&nbsp;The format of TU info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Operon_name &nbsp;&nbsp;&nbsp;Unit_name &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;Transcription_start_site ......</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/1/1e/USTC_Software_TI.jpg"/><br />
The principle of fetching information of promoters is same as fetching genes's. Our software stores the promoter information from the file named “promoter_info” in a “map” which could be used to pick out the promoter sequence by searching promoter name through binary tree method.</br></br><br />
&nbsp;&nbsp;The format of Promoter Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Promoter_ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Promoter_name</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/8/8a/USTC_Software_PI.jpg"/><br />
The sequence of promoter will be used in the alignment method in next module which could make a prediction of exogenous genes' regulation pattern.<br />
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<div class="jobs_trigger"> <strong>Integration</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify"> <br />
Our software integrates all information we picked out about genes and generates a file named “all_info” —— all information about genes —— for the output graphical interface's reading. In the meanwhile, the array of objects containing all information has been stored in computer memory which greatly improve the computing speed of our software.</br></br><br />
&nbsp;&nbsp;The format of all_info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;No. &nbsp;&nbsp;&nbsp;promoter_sequence &nbsp;&nbsp;&nbsp;gene_sequence &nbsp;&nbsp;&nbsp;gene_name &nbsp;&nbsp;&nbsp;ID &nbsp;&nbsp;&nbsp;left_position &nbsp;&nbsp;&nbsp;right_position &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;&nbsp;description</br><br />
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The fetching module generates three files: old_GRN, all_info and uncertain_database.</br><br />
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<div id="Alignment_Analyze"><br />
<h2>Operon Theory and Regulatory Model</h2><br />
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<div class="jobs_trigger"><strong>Operon Theory</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><br />
<p align="justify">In genetics, an operon is a functioning unit of genomic DNA containing a cluster of genes<br />
under the control of a single regulatory signal or promoter. The genes contained in the<br />
operon are either expressed together or not at all. Several genes must be both cotranscribed<br />
and co-regulated to define an operon.<br /><br /><br />
The first time "operon" was proposed is in a paper of French Academic Science, 1960.<br />
The lac operon of the model bacterium E. coli was discovered and provides a typical<br />
example of operon function. It consists a promoter, an operator, three structural genes and<br />
a terminator. The operon is regulated by several factors including the availability of glucose<br />
and lactose.<br /><br /><br />
From this paper, the so-called general theory of the operon was developed. According to<br />
the theory, all genes are controlled by means of operons through a single feedback<br />
regulatory mechanism-repression. The first operon to be described was the lac operon in<br />
E. coli. The 1965 Nobel Prize in Physiology and Medicine was awarded to François Jacob,<br />
André Michel Lwoff and Jacques Lucien Monod for their discoveries concerning the operon and virus synthesis.<br /><br />
</p><br />
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<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/7/7d/USTC_Software_Figure_1.png" /><br />
<p align="center"><strong>Figure 1.</strong> Structure of Operon</p></div><br />
<p align="justify">An operon is made up of several structural genes arranged under a common promoter and<br />
regulated by a common operator. It is defined as a set of adjacent structural genes, plus<br />
the adjacent regulatory signals that affect transcription of the structural genes. The<br />
regulators of a given operon, including repressors, corepressors and activators, are not<br />
necessarily coded for by that operon.<br /><br /><br />
As a unit of transcription, upstream of the structural genes lies a promoter sequence which<br />
provides a site for RNA polymerase to bind and initiate transcription. Close to the promoter<br />
lies a section of DNA called an operator.<br /><br /><br />
Operon regulation can be either negative or positive by induction or repression. Negative<br />
control involves the binding of a repressor to the operator to prevent transcription.<br />
Operons can also be positively controlled. An activator protein binds to DNA, usually at a<br />
site other than the operator, to stimulate transcription.<br />
</p><br />
<div align="center"><img style="width:600px;" src="https://static.igem.org/mediawiki/igem.org/2/25/USTC_Software_Figure_2.png"/><br />
<p align="justify"><strong>Figure 2.</strong> Regulation of Operon<br />
1: RNA Polymerase, 2: Repressor, 3: Promoter, 4: Operator, 5: Lactose, 6: lacZ, 7:<br />
lacY, 8: lacA. Top: The gene is essentially turned off. There is no lactose to inhibit the<br />
repressor, so the repressor binds to the operator, which obstructs the RNA polymerase<br />
from binding to the promoter and making lactase.Bottom: The gene is turned on.Lactose<br />
is inhibiting the repressor, allowing the RNA polymerase to bind with the promoter, and<br />
express the genes, which synthesize lactase. Eventually, the lactase will digest all of the<br />
lactose, until there is none to bind to the repressor. The repressor will then bind to the<br />
operator, stopping the manufacture of lactase.</p></div><br />
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<div class="jobs_trigger"><strong>Regulatory Model</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">Regulation of gene expression includes four levels. We choose the transcriptional level to simulate the regulation both for its significance and model simplification.</p><br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/igem.org/8/87/USTC_Software_Figure_3.png" /><br />
<p><strong>Figure 3.</strong>Regulation of gene expression.<br />Our regulation model is built based on the operon theory.<br /> The promoter region is regarded as the main regulatory region.</p></div><br />
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<div class="jobs_trigger"> <strong>Similarity and Homology</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment. It is defined as the proportion of the common subsequence in the aligned sequence. Any two sequences share a certain<br />
similarity. It should be noted that similarity and homology are two different concepts.<br /><br /><br />
As with anatomical structures, homology between protein or DNA sequences is defined in<br />
terms of shared ancestry. Two segments of DNA can have shared ancestry because of<br />
either a speciation event or a duplication event. The terms “percent homology” and<br />
“sequence similarity” are often used interchangeably. As with anatomical structures, high<br />
sequence similarity might occur because of convergent evolution, or, as with shorter<br />
sequences, because of chance. Such sequences are similar but not homologous.<br />
Sequence regions that homologous are also called conserved.<br /><br /><br />
In our project, we use similarity to connect the exogenous gene with the original network.<br />
Because there is a good chance that the exogenous gene is not homologous with the<br />
genes in the network.</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The GRN matrix is the mathematical description of gene regulatory network in which “1” represents “enhance”, “-1” represents “repress” and “0” represents “no regulatory relationship”. The units(RU) in x-axis regulate the units in y-axis. A row can be seen as a vector containing all the information of the target(corresponding unit in the y-axis). Similarly, a column can be seen as a vector containing all the information of the regulator(corresponding unit in the x-axis).</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment based on Needleman-Wunsch algorithm[FIXME: wiki link here]. The Needleman-Wunsch algorithm performs a global alignment on two protein sequences or nucleotide sequences. It was the first application of dynamic programming to biological sequence comparison.<br /><br /><br />
<br />
When dynamic programming is applicable, the method takes far less time than naive methods. Using a naive method, many of the subproblems are generated and sovled many times. The dynamic programming approach seeks to solve each subproblem only once. Once the solution to a given subproblem has been computed, it is stored to be looked up next time.<br /><br /><br />
<br />
Like the Needleman-Wunsch algorithm, of which it is a variation, Smith-Waterman is also a dynamic programming algorithm. But it is a local sequence alignment algorithm. The famous BLAST(Basic Local Alignment Search Tool) is improved from Smith-Waterman algorithm. Although local algorithm has the desirable property that it is guaranteed to find the optimal local alignment, we decided to choose the global one because we regarded the segment sequence as a unit.<br /><br /><br />
<br />
Sequences are aligned with different detailed methods in different situations. In the regulated side, what we care about is the DNA sequence. In the regulating side, it is the amino acid sequence. When it comes to predict the regulated behavior, we use a DNA substitution matrix to align promoter and protein coding sequences. In the prediction of regulating behavior, the substitution matrix BLOSUM_50 is used to align the amino acid sequences translated from protein coding sequences.<br /><br /><br />
<br />
The promoter similarities of the query unit and subject units are stored in a vector. The protein coding similarities are stored in another vector. These vectors are prepared to be used in the new network construction.<br />
</p> <br />
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<h2>Forward Analysis</h2><br />
<div class="jobs_trigger"><strong>Construct New GRN</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<h3>User Input</h3><br />
<p align="justify"><br />
Some genes' regulation could be get from experiment. So, if users could get the unknow regulation between new gene and old ones, they could manually set the interactions which do not need model. Those regulations will be used in later simulation.<br />
</p><br />
<h3>Simalarity Analysis</h3><br />
<p align="justify"><b>1.Sequence</b></br><br />
<h4>Needleman-Wunsch Algorithm</h4><br />
The Needleman-Wunsch algorithm was first published in1970 by Saul B. Needleman and Christian D. Wunsch. It performs a global alignment of two sequences and is mostly used in bioinformatics to align protein or nucleotide sequence. Our software applied this algorithm in the alignment of DNA and amino acid sequences.<br/><br/><br />
<br />
The Needleman-Wunsch algorithm is one kind of dynamic programming and It was the first attempt in biological sequence comparison of dynamic programming.<br/><br/><br />
<br />
Here is an example of Needleman-Wunsch algorithm. S(a,b) is the similarity of character a and character b. The scores of characters are shown in the similarity matrix. We assume this matrix was<br />
</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/5/52/USTC_Software_DNA_S_M.png"/></div> <br />
<p>And we uses linear gap penalty, denoted by d, here, we set the gap penalty as -5.Then the alignment:</p><br />
<p align="center"><strong><em><br />
A: AGACTAGTTAC<br/><br />
B: CGA - - - GACGT<br />
</em></strong></p><br />
<br />
<p>would have the following score:</p><br />
<p align="center"><strong><em><br />
S(A,C)+S(G,C)+S(A,A)+(3)+S(G,G)+S(T,A)+S(T,C)+S(A,G)+S(C,T) = -3+7+10-(3x5)+7+(-4)+0+(-1)+0 = 1<br />
</em></strong></p><br />
<br />
<p align="justify">To find the highest score of alignment, in this algorithm, a two dimensional matrix F with sequences and scores was allocated. The score in row i, column j is denoted by Fij. There is one column for each character in sequence A and one row for each character in sequence B. Therefore, if we align sequences with sizes of n and m, the amount of memory taken up here is O(n,m).<br/><br/><br />
<br />
As the algorithm going on, Fij was calculated to be the optimal score by the principle as following:<br/><br />
Basis:<br />
</p><br />
<p align="center"><strong><em>Fi0 = d*i<br/>F0j = d*j</em></strong></p><br />
<p>Recursion:</p><br />
<p align="center"><strong><em>Fij = max(F(i-1,j-1) + S(Ai,Bj), F(i-1,j) + d, F(i,j-1) + d)</em></strong></p><br/><br />
<p>The pseudo-code of this algorithm would look like this:</p><br />
<br/><br />
<div id="pseudo"><p><br />
<strong> for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; F(i,0) <-- d*i<br/><br />
<strong> for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; F(0,j) <-- d*j<br/><br />
<strong>for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; <strong>for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; {<br/><br />
&nbsp; &nbsp; Match <-- F(i-1,j-1) + S(Ai,Bj)<br/><br />
&nbsp; &nbsp; Delete <-- F(i-1,j) + d<br/><br />
&nbsp; &nbsp; Insert <-- F(i,j-1) + d<br/><br />
&nbsp; &nbsp; F(i,j) <-- <strong>max</strong>(Match, Insert, Delete)<br/><br />
&nbsp; }<br />
</p><br />
</div><br />
<br />
<p align="justify">After the matrix F was computed, Fnm would be the maximum score among all possible alignment.<br/><br/><br />
<br />
If you want to see the optimal alignment, you can trace back from Fnm by comparing three possible sources mentioned in the above code (Match, Insert and Delete). If Match, then Aj and Bi are aligned, if Insert, Bi was aligned with a gap and if Delete, then Aj and a gap are aligned. Also, you may find there are not only one optimal alignment.<br/><br/><br />
As for the example, we would get the following matrix by applying Needleman Wunsch algorithm:</p><br />
<br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/e/e2/USTC_Software_arrow_game.png"/></div><br />
<p>And the optimal alignment would be:</p><br />
<br />
<p align="center"><strong><em>- - AGACTAGTTAC <br/><br />
CGAGAC - - GT - - -<br />
</em></strong></p><br />
<h4>A Supplementary Game</h4><br />
<p align="justify">The rows and columns in the GRN matrix can be regarded as vectors containing the regulated or the regulating information. The behavior similarity of two units can be described by the dot product of two regulated vectors or two regulating vectors. Biologists usually think the more similar two sequences are, the more likely they have similar behaviors. Whether the ratio of genes with similar behaviors is positively correlated with gene similarity is essential to our project. So we obtained 1.6 million sets of data by pairwise alignment of all the 1748 units in the GRN of K-12. Each set of data consists of gene similarity and behavior similarity. The result is analyzed and plotted in the figure. The linear fit shows that the ratio is positively correlated with the similarity.</p><br/><br />
<br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/2013/d/d0/USTC_Software_Simi-Ratio.png" /><br />
<p><strong>Figure 4.</strong>Linear fit of ratio-similarity relationship.</p></div><br />
<p align="justify">Although there are examples that a slight change in DNA sequence will significantly change the property of the gene, for example, sickle-cell disease, the influence is usually determined by the location and scale of the mutation. So the result is still convincing to some degree.</p><br />
<p><br />
<b>2.Filtering</b></p><br />
<h4>Random Noise</h4><br />
<p class="bodytext"></p><p align="justify">Normally, the similarity of two sequences will not be zero. Some computational<br />
experiments were carried out to study the random sequence similarities. We randomly<br />
chose a gene in the network and generated 1000 random sequences. The alignment result<br />
indicates that the random sequence similarities are Gauss distributed. The result suggests<br />
that some similarities are out of statistic significance.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/8/89/USTC_Software_Figure_4.png" /><br />
<p><strong>Figure 5.</strong> Random similarity distribution</p></div><br />
<h4>Filter</h4><br />
<p align="justify">We need the genes highly similar to the exogenous one to interact with it. The program will<br />
align the exogenous gene(query) with genes in the network(subject) and get the original<br />
similarities. In order to filter meaningless low values, a certain amount of random<br />
sequences are generated for each query-subject alignment. Normally, 100 is sufficient.<br />
Because the sequence length will influence alignment result, random sequences are fixed<br />
at the same length as the query one. Then align random sequences with the subject<br />
sequence. The statistic result of these random similarities is used as a threshold.<br /><br />
<div align="center">Threshold = μ + xσ</div><br /><br />
In the formula, μ is the average random similarity. σ is the standard deviation. x is used to<br />
control the filter determined by machine learning. If the original similarity is lower than the<br />
threshold, it is abandoned. It is usually means the original value is usually short of<br />
statistical significance.<br /><br /><br />
An example about filtring and consistency is presented in “Example”.<br />
</p><br />
<p><b>3.Regulation Calculation</b></p><br />
<p align="justify">If there is a three-unit network and they interact with each other as it is shown in the figure.<br />
The regulation is described by the GRN matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/8/8a/USTC_Software_Figure_5.png" /><br />
<p align="justify"><strong>Figure 6.</strong> Example network and its GRN matrix.</p></div><br />
<br />
<br />
<p align="justify">If D is the exogenous unit, we can obtain three similarity data sets of D with the units in the<br />
original GRN: <br />
<li style="margin-left:40px;">Promoter sequence similarity</li><br />
<li style="margin-left:40px;">Gene sequence similarity</li><br />
<li style="margin-left:40px;">Amino acid sequence similarity.</li><br />
<p><br />
The construction is equivalent to add a new column and a row into the original matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/9/97/USTC_Software_Figure_6.png" /><br />
<p><strong>Figure 7.</strong> Mathematical Equivalence</p></div><br />
<p align="justify">When filling the column, D is compared with the regulators of the unit in each row. The<br />
regulations in the row are consider separately and marked as “positive group” and<br />
“negative group”. The average similarity of each group represents the distance between<br />
the exogenous unit and the group. D is supposed to have the larger one's regulatory<br />
direction(positive or negative). The regulatory intensity is the weight average regulation of<br />
the chose group. The weight here is the amino acid sequence similarity.<br /><br /><br />
There are two conditions when fill the new row:<br /><br />
1. There are units having the same promoter as the exogenous unit.<br /><br />
2. There is no units having the same promoter as the exogenous unit.<br /><br /><br />
In condition 1, the units sharing the same promoter with the new member are picked out,<br />
and the following steps are the same as the construction of the column. The difference is<br />
the similarity used here is the gene sequence similarity. As explained in the regulation<br />
model part, the promoter is the main regulatory region, but the following sequence is also<br />
considered. Now the promoter is the same, so what we focus on are the gene sequences.<br /><br /><br />
In condition 2, the process is almost the same as constructing the new column. Promoter<br />
similarity is used because it is the main region.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/c/c5/USTC_Software_Figure_7.png" /><br />
<p><strong>Figure 8.</strong> Construct New GRN</p><br />
<h3>Clustering</h3><br />
<p><br />
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.</br></br><br />
For get a better regulation, we use online database DAVID to cluster all the genes in our whole GRN. Avoid of supersoftless, we hope to create an online communication with DAVID. After getting the cluster of our genes, we multiply the genes simalarity with a factor if they are in the same cluster.</br></br><br />
Though the source code of this part has already done, we lack the experiment information to set a propriate factor. All source code were pushed up to our github.<br />
</p><br />
</div><br />
<div class="jobs_trigger"><strong>Network Model</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Network analysis includes finding stable condition of network, adding new gene, finding new stable condition and changes from original condition to new condition. We use densities of materials to describe network condition. If all material densities are time-invariant, we can say the network condition is stable.</p><br />
<p class="bodytext"></p><p align="justify">Regulation relationship in genetic network includes positive regulation, negative regulation, positive-or-negative regulation and no regulation. We store regulation relationship in matrix R. Rji means the unit in line j and row i. For the material of original network, Rji=1 means material i enhance material j, Rji=-1 means material i repress material j, Rji=0 means material i has no influence on material j, Rji=2 means material i enhance or repress material j. For the new material, Rji ranges from -1 to 1. Rji<0 means the possibility of positive regulation is Rji; Rji>0 means the possibility of negative regulation is –Rji; Rji=0 means there is no regulation from i to j.<br />
We use Hill equations to describe intensity of regulation. Equations are like following:<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e0/USTC_Software_1.png" style="width:600px;"/><br />
<br/></br><br />
The left side of the equation is the derivative x(density) on t(time).”qi”,”pi”,”ri”,”mi”,”ni” are parameters, which determine the intensity of regulation."ri" is degradation rate. Mji is exponent. M is a matrix whose dimensions are equivalent to R's. Mji is 0 or ranges from 0.5 to 1.2 or ranges from -1.2 to 0.5. For the material of original network, if Rji=1,Mji ranges from 0.5 to 1.2;if Rji=-1, Mji ranges from -1.2 to -0.5; if Rji=2;Mji ranges from -1.2 to 0.5 or 0.5 to 1. These Mjis' absolute values are given randomly by program. If Rji=0, Mji=0. <br />
</br>For the new material,<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/64/USTC_Software_2.png"/><br />
<br/></br><br />
<br />
</p><br />
<p align="justify"><br />
Stable condition is the condition in which densities are time-invariant. We store material densities in a vector and solve the differential equations with Euler's formula, which is like below<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e6/USTC_Software_3.png" style="width:600px;"/><br />
<br/></br><br />
We know the network will be stable at last, so every material density has a limitation.<br />
<br />
</p><br />
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</div><br />
<div class="jobs_trigger"><strong>Evaluate Network</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Record the original stable condition, set new material density to 0 and this is the new initial density vector. Solve new equations and record density vectors before the new condition is stable and store these data in a text file.</br></br><br />
<br />
To evaluate the new network, we introduce the grading system.<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/3/32/USTC_Software_4.png" style="width:600px;"/><br />
<img src="https://static.igem.org/mediawiki/2013/b/bc/USTC_Software_5.png" style="width:500px;"/><br />
<br />
<br/></br><br />
"xi" and "Xi" are densities of material i, which is not the new material."ny" is the number of materials. The more new densities are close to the original, the less the influence the cell endues. In general, cells close to the original cell are more likely to survive than those who are far different from the original cell. That is the thought of the grading system.</br></br><br />
We did a lot of running and found that the “AbsValue” ranges from 0 to 370, so "ScoreA" ranges from 0 to 4.9.We get the integer part and store it in an array, which has five sections. Generate 100 or 200 matrix M from matrix R and run the original and new network for each M, so we can get 100 or 200 of "ScoreA"s. The section which has maximum "ScoreA"s is the eventual score.<br />
</p> <br />
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<br />
<h2>Reverse Analysis</h2><br />
<div class="jobs_trigger"><strong>Virtual Gene</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
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<div class="jobs_trigger"><strong>Expression Range</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Before prediction, the expression of specific genes which the experimenter needs should be input into our software as well as the improvement or depression. The number of target gene is SIX at most.</br></br><br />
It is a must that figuring out the strongest and weakest expression strength before inputting the extreme cases into the target expression. The way to find out the strongest and weakest expression is modeling the GRN's steady state by a large amount of random regulation from -1 and 1. We ran it for 1000 times to get the range of gene expression. On the other hand, the expression of genes unpicked by the users should be stable as much as possible. The initial strength of expression is calculated by modeling the original GRN with Hill's equation.<br />
</p><br />
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<div class="jobs_trigger"><strong>Particle Swarm Optimaztion</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify"><br />
For getting the best regulation, our software uses PSO algorithm based on 30 particles to simulate the GRN's changing. First of all, the interactions of regulator and regulated-by have been put into those particles in random so that each particle will have the whole set of regulation. Secondly, the variance between target expressions and stable expression of new GRN have been regarded as the optimize requirements in PSO algorithm. As a result, the minimal variance of 30 particles is the global optimum and the minimal variance of the procession in one particle is the local optimum. Then, taking a step towards global and local optimum as well as considering the inertia and perturbation avoids falling into the sub-optimal condition.</br></br><br />
At last, when the variance of expression reaches an acceptable range, our software picks out and saves the best global optimum particle following by the movement of those particles stop.</br></br><br />
We constantly revises the factors in PSO algorithm by machine learning method for accurate simulation with a fast PSO particle-motion equation. At the same time, our software also filter the result of regulatory value which is more intuitive.<br />
</p><br />
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<div class="jobs_trigger"><strong>Locate Optimal Target</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">To improve the efficiency of choosing a suitable gene after getting a series of regulatory value, our software picks out some obvious regulation. The value of regulation is between -1 to 1 in which -1 means negative effect and 1 means positive effect. As a result, what our software has done is filtering out the absolute value which is lower than 0.9. Because the difference of regulatory intensity lower than 0.1 has very little effect to the stable expression, the final result of regulation is indicated by Boolean value.</br></br><br />
The format of regulatory prediction in “Result”:</br><br />
Gene_name->Gene_name regulation(+/-)<br />
<br />
</p> <br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/kuntestTeam:USTC-Software/kuntest2013-10-27T13:56:08Z<p>USTCkun: </p>
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<h1 align="justify">Methodologies</h1><br />
<p align="justify">In order to simulate the GRN's working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</br><br/><br />
There are four parts of methodologies: Database, Operon Theory and Regulatory Model, Forward Analysis and Reverse Analysis.<br />
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<h2>Database</h2><br />
<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Abstract</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">To simulate and analyze a genetic regulatory network (GRN), we need to build an objects' array to store the complete information of each gene. It contains regulation relationships between genes, sequences of genes, sequences of promoters and so on. However, it's hard to find an appropriate database online containing all information we need in a simple file. RegulonDB has downloadable files about the regulation between transcription factors (TF) and genes. Files about genetic information, transcription unit information and promoter information can also be downloaded from the RegulonDB. All those files have been put into file “source data” in the root directory of our software. They contain all information the simulation needs and we use fetching module to achieve data extraction and integration. There are four steps: fetch regulation relationships, fetch gene information, fetch promoter information and integrate information above.<br />
</p><br />
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<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Fetch Regulation</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">In GRN, there are two kinds of files: <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_tf.txt"> TF to TF</a> and <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_gene.txt">TF to Gene</a>. Since the database about the regulation between TFs and Genes contains only one-way interaction, the matrix of GRN is a rectangle.</br></br><br />
First of all, read the regulation relationship of TFs. Our software filters the documentation of RegulonDB on the head of all files and then reads the name of regulate and regulated TF, which is also the name of its genes, one by one. In the same time, our software numerates the genes and stores their names into an objects' array of genetic data. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/69/USTC_Software_TT.jpg"/><br />
The regulation of TFs has been put into a square matrix whose row is the regulator and column is the one regulated by. To make our GRN as complete as possible, the regulation between TF and genes has joined into the matrix. The one-way interaction results that we must read the TF in order to fulfill the regulator before completing the TF to gene's regulation in the same way of TF to TF. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/47/USTC_Software_TG.jpg"/><br />
At last, a regulatory matrix whose row represents regulate gene (TF) and whose column represents gene regulated by (TF+Gene) has been output into a file called “old_GRN” in root directory. The values in GRN matrix are regulations in which “1” means positive activation, “-1” means repression and “0” means no relationship. There have been some regulations both positive and negative identified regulations are determined by the experimental environment. As a result, our software picks out those uncertain genes and stores them into a file named “uncertain_database”.</br></br><br />
&nbsp;&nbsp;The format of uncertain database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;? &nbsp;&nbsp;&nbsp;Gene_name->Gene_name</br></br><br />
<br />
The question mark represents the unknown regulation between regulator and regulated-by whose names presented afterward. Users could replace the question mark with the data known in past experiment. (“+” rep positive, “-” rep negative). Our software will replace the values in matrix automatically. But if not rewrote, our software will regard those regulation as unknown.<br />
</p><br />
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<br />
<div class="jobs_trigger"><strong> Fetch Gene Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify"><br />
All gene information has been deposited into a file named gene_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/Gene_sequence.txt">here</a>. In order of picking out the genes in GRN as fast as possible, all genetic information are stored in a “map”. “Map” is just like a dictionary yet its words are names of genes and its descriptions of words are replaced by genetic information. By using binary tree method, it is very fast to search the “word” wanted in the “dictionary”. As tested, the speed of binary tree method built-in “map” function is 720 times faster than traversal method.</br></br><br />
&nbsp;&nbsp;The format of Gene Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;Left_end_position &nbsp;&nbsp;&nbsp;Right_end_position &nbsp;&nbsp;&nbsp;DNA_strand &nbsp;&nbsp;&nbsp;Product_type &nbsp;&nbsp;&nbsp;&nbsp;Product_name &nbsp;&nbsp;&nbsp;Start_codon_sequence&nbsp;&nbsp;&nbsp; Stop_codon_sequence &nbsp;&nbsp;&nbsp;Gene_sequence</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/45/USTC_Software_GI.jpg"/><br />
The label of the map vector is gene name which will be picked out based on the names read in regulation matrix before. It is really fast using the binary tree method to find the specific genetic information and store them into a specific object. Those information includes gene ID, left position, right position, gene description and gene sequence. The gene ID is used to link to RegulonDB's gene details; The left position is used to find its specific transcription unit; The right position is used to figure out the base amount; The description of genes is used to distinguish the RNA and protein; The sequence is used to predict the regulation by alignment.<br />
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</p><br />
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<br />
<div class="jobs_trigger"> <strong>Fetch Promoter Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">All promoter information has been deposited into a file named promoter_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/PromoterSet.txt">here</a>. But we also need transcription unit information because the information files about promoter do not contain all genes' names backward. “TU Info” file, which can be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/TUSet.txt">here</a>, contains the starting position of each TU and its promoter name. Our software picks out the starting position into a integer array. Using the left position picked out in gene info, our software would find out which unit the gene belongs to through dichotomy method and then stores the name of promoter into corresponding object.</br></br><br />
&nbsp;&nbsp;The format of TU info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Operon_name &nbsp;&nbsp;&nbsp;Unit_name &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;Transcription_start_site ......</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/1/1e/USTC_Software_TI.jpg"/><br />
The principle of fetching information of promoters is same as fetching genes's. Our software stores the promoter information from the file named “promoter_info” in a “map” which could be used to pick out the promoter sequence by searching promoter name through binary tree method.</br></br><br />
&nbsp;&nbsp;The format of Promoter Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Promoter_ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Promoter_name</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/8/8a/USTC_Software_PI.jpg"/><br />
The sequence of promoter will be used in the alignment method in next module which could make a prediction of exogenous genes' regulation pattern.<br />
</p> </div> <br />
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<div class="jobs_trigger"> <strong>Integration</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify"> <br />
Our software integrates all information we picked out about genes and generates a file named “all_info” —— all information about genes —— for the output graphical interface's reading. In the meanwhile, the array of objects containing all information has been stored in computer memory which greatly improve the computing speed of our software.</br></br><br />
&nbsp;&nbsp;The format of all_info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;No. &nbsp;&nbsp;&nbsp;promoter_sequence &nbsp;&nbsp;&nbsp;gene_sequence &nbsp;&nbsp;&nbsp;gene_name &nbsp;&nbsp;&nbsp;ID &nbsp;&nbsp;&nbsp;left_position &nbsp;&nbsp;&nbsp;right_position &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;&nbsp;description</br><br />
<br />
The fetching module generates three files: old_GRN, all_info and uncertain_database.</br><br />
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<h2>Operon Theory and Regulatory Model</h2><br />
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<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Operon Theory</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><br />
<p align="justify">In genetics, an operon is a functioning unit of genomic DNA containing a cluster of genes<br />
under the control of a single regulatory signal or promoter. The genes contained in the<br />
operon are either expressed together or not at all. Several genes must be both cotranscribed<br />
and co-regulated to define an operon.<br /><br /><br />
The first time "operon" was proposed is in a paper of French Academic Science, 1960.<br />
The lac operon of the model bacterium E. coli was discovered and provides a typical<br />
example of operon function. It consists a promoter, an operator, three structural genes and<br />
a terminator. The operon is regulated by several factors including the availability of glucose<br />
and lactose.<br /><br /><br />
From this paper, the so-called general theory of the operon was developed. According to<br />
the theory, all genes are controlled by means of operons through a single feedback<br />
regulatory mechanism-repression. The first operon to be described was the lac operon in<br />
E. coli. The 1965 Nobel Prize in Physiology and Medicine was awarded to François Jacob,<br />
André Michel Lwoff and Jacques Lucien Monod for their discoveries concerning the operon and virus synthesis.<br /><br />
</p><br />
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<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/7/7d/USTC_Software_Figure_1.png" /><br />
<p align="center"><strong>Figure 1.</strong> Structure of Operon</p></div><br />
<p align="justify">An operon is made up of several structural genes arranged under a common promoter and<br />
regulated by a common operator. It is defined as a set of adjacent structural genes, plus<br />
the adjacent regulatory signals that affect transcription of the structural genes. The<br />
regulators of a given operon, including repressors, corepressors and activators, are not<br />
necessarily coded for by that operon.<br /><br /><br />
As a unit of transcription, upstream of the structural genes lies a promoter sequence which<br />
provides a site for RNA polymerase to bind and initiate transcription. Close to the promoter<br />
lies a section of DNA called an operator.<br /><br /><br />
Operon regulation can be either negative or positive by induction or repression. Negative<br />
control involves the binding of a repressor to the operator to prevent transcription.<br />
Operons can also be positively controlled. An activator protein binds to DNA, usually at a<br />
site other than the operator, to stimulate transcription.<br />
</p><br />
<div align="center"><img style="width:600px;" src="https://static.igem.org/mediawiki/igem.org/2/25/USTC_Software_Figure_2.png"/><br />
<p align="justify"><strong>Figure 2.</strong> Regulation of Operon<br />
1: RNA Polymerase, 2: Repressor, 3: Promoter, 4: Operator, 5: Lactose, 6: lacZ, 7:<br />
lacY, 8: lacA. Top: The gene is essentially turned off. There is no lactose to inhibit the<br />
repressor, so the repressor binds to the operator, which obstructs the RNA polymerase<br />
from binding to the promoter and making lactase.Bottom: The gene is turned on.Lactose<br />
is inhibiting the repressor, allowing the RNA polymerase to bind with the promoter, and<br />
express the genes, which synthesize lactase. Eventually, the lactase will digest all of the<br />
lactose, until there is none to bind to the repressor. The repressor will then bind to the<br />
operator, stopping the manufacture of lactase.</p></div><br />
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<br />
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<br />
<div class="jobs_trigger"><strong>Regulatory Model</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">Regulation of gene expression includes four levels. We choose the transcriptional level to simulate the regulation both for its significance and model simplification.</p><br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/igem.org/8/87/USTC_Software_Figure_3.png" /><br />
<p><strong>Figure 3.</strong>Regulation of gene expression.<br />Our regulation model is built based on the operon theory.<br /> The promoter region is regarded as the main regulatory region.</p></div><br />
</div><br />
<br />
<br />
<br />
<div class="jobs_trigger"> <strong>Similarity and Homology</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment. It is defined as the proportion of the common subsequence in the aligned sequence. Any two sequences share a certain<br />
similarity. It should be noted that similarity and homology are two different concepts.<br /><br /><br />
As with anatomical structures, homology between protein or DNA sequences is defined in<br />
terms of shared ancestry. Two segments of DNA can have shared ancestry because of<br />
either a speciation event or a duplication event. The terms “percent homology” and<br />
“sequence similarity” are often used interchangeably. As with anatomical structures, high<br />
sequence similarity might occur because of convergent evolution, or, as with shorter<br />
sequences, because of chance. Such sequences are similar but not homologous.<br />
Sequence regions that homologous are also called conserved.<br /><br /><br />
In our project, we use similarity to connect the exogenous gene with the original network.<br />
Because there is a good chance that the exogenous gene is not homologous with the<br />
genes in the network.</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The GRN matrix is the mathematical description of gene regulatory network in which “1” represents “enhance”, “-1” represents “repress” and “0” represents “no regulatory relationship”. The units(RU) in x-axis regulate the units in y-axis. A row can be seen as a vector containing all the information of the target(corresponding unit in the y-axis). Similarly, a column can be seen as a vector containing all the information of the regulator(corresponding unit in the x-axis).</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment based on Needleman-Wunsch algorithm[FIXME: wiki link here]. The Needleman-Wunsch algorithm performs a global alignment on two protein sequences or nucleotide sequences. It was the first application of dynamic programming to biological sequence comparison.<br /><br /><br />
<br />
When dynamic programming is applicable, the method takes far less time than naive methods. Using a naive method, many of the subproblems are generated and sovled many times. The dynamic programming approach seeks to solve each subproblem only once. Once the solution to a given subproblem has been computed, it is stored to be looked up next time.<br /><br /><br />
<br />
Like the Needleman-Wunsch algorithm, of which it is a variation, Smith-Waterman is also a dynamic programming algorithm. But it is a local sequence alignment algorithm. The famous BLAST(Basic Local Alignment Search Tool) is improved from Smith-Waterman algorithm. Although local algorithm has the desirable property that it is guaranteed to find the optimal local alignment, we decided to choose the global one because we regarded the segment sequence as a unit.<br /><br /><br />
<br />
Sequences are aligned with different detailed methods in different situations. In the regulated side, what we care about is the DNA sequence. In the regulating side, it is the amino acid sequence. When it comes to predict the regulated behavior, we use a DNA substitution matrix to align promoter and protein coding sequences. In the prediction of regulating behavior, the substitution matrix BLOSUM_50 is used to align the amino acid sequences translated from protein coding sequences.<br /><br /><br />
<br />
The promoter similarities of the query unit and subject units are stored in a vector. The protein coding similarities are stored in another vector. These vectors are prepared to be used in the new network construction.<br />
</p> <br />
</div> <br />
<br />
</div><br />
<br />
<br />
<h2>Forward Analysis</h2><br />
<div class="jobs_trigger"><strong>Construct New GRN</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<h3>User Input</h3><br />
<p align="justify"><br />
Some genes' regulation could be get from experiment. So, if users could get the unknow regulation between new gene and old ones, they could manually set the interactions which do not need model. Those regulations will be used in later simulation.<br />
</p><br />
<h3>Simalarity Analysis</h3><br />
<p align="justify"><b>1.Sequence</b></br><br />
<h4>Needleman-Wunsch Algorithm</h4><br />
The Needleman-Wunsch algorithm was first published in1970 by Saul B. Needleman and Christian D. Wunsch. It performs a global alignment of two sequences and is mostly used in bioinformatics to align protein or nucleotide sequence. Our software applied this algorithm in the alignment of DNA and amino acid sequences.<br/><br/><br />
<br />
The Needleman-Wunsch algorithm is one kind of dynamic programming and It was the first attempt in biological sequence comparison of dynamic programming.<br/><br/><br />
<br />
Here is an example of Needleman-Wunsch algorithm. S(a,b) is the similarity of character a and character b. The scores of characters are shown in the similarity matrix. We assume this matrix was<br />
</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/5/52/USTC_Software_DNA_S_M.png"/></div> <br />
<p>And we uses linear gap penalty, denoted by d, here, we set the gap penalty as -5.Then the alignment:</p><br />
<p align="center"><strong><em><br />
A: AGACTAGTTAC<br/><br />
B: CGA - - - GACGT<br />
</em></strong></p><br />
<br />
<p>would have the following score:</p><br />
<p align="center"><strong><em><br />
S(A,C)+S(G,C)+S(A,A)+(3)+S(G,G)+S(T,A)+S(T,C)+S(A,G)+S(C,T) = -3+7+10-(3x5)+7+(-4)+0+(-1)+0 = 1<br />
</em></strong></p><br />
<br />
<p align="justify">To find the highest score of alignment, in this algorithm, a two dimensional matrix F with sequences and scores was allocated. The score in row i, column j is denoted by Fij. There is one column for each character in sequence A and one row for each character in sequence B. Therefore, if we align sequences with sizes of n and m, the amount of memory taken up here is O(n,m).<br/><br/><br />
<br />
As the algorithm going on, Fij was calculated to be the optimal score by the principle as following:<br/><br />
Basis:<br />
</p><br />
<p align="center"><strong><em>Fi0 = d*i<br/>F0j = d*j</em></strong></p><br />
<p>Recursion:</p><br />
<p align="center"><strong><em>Fij = max(F(i-1,j-1) + S(Ai,Bj), F(i-1,j) + d, F(i,j-1) + d)</em></strong></p><br/><br />
<p>The pseudo-code of this algorithm would look like this:</p><br />
<br/><br />
<div id="pseudo"><p><br />
<strong> for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; F(i,0) <-- d*i<br/><br />
<strong> for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; F(0,j) <-- d*j<br/><br />
<strong>for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; <strong>for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; {<br/><br />
&nbsp; &nbsp; Match <-- F(i-1,j-1) + S(Ai,Bj)<br/><br />
&nbsp; &nbsp; Delete <-- F(i-1,j) + d<br/><br />
&nbsp; &nbsp; Insert <-- F(i,j-1) + d<br/><br />
&nbsp; &nbsp; F(i,j) <-- <strong>max</strong>(Match, Insert, Delete)<br/><br />
&nbsp; }<br />
</p><br />
</div><br />
<br />
<p align="justify">After the matrix F was computed, Fnm would be the maximum score among all possible alignment.<br/><br/><br />
<br />
If you want to see the optimal alignment, you can trace back from Fnm by comparing three possible sources mentioned in the above code (Match, Insert and Delete). If Match, then Aj and Bi are aligned, if Insert, Bi was aligned with a gap and if Delete, then Aj and a gap are aligned. Also, you may find there are not only one optimal alignment.<br/><br/><br />
As for the example, we would get the following matrix by applying Needleman Wunsch algorithm:</p><br />
<br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/e/e2/USTC_Software_arrow_game.png"/></div><br />
<p>And the optimal alignment would be:</p><br />
<br />
<p align="center"><strong><em>- - AGACTAGTTAC <br/><br />
CGAGAC - - GT - - -<br />
</em></strong></p><br />
<h4>A Supplementary Game</h4><br />
<p align="justify">The rows and columns in the GRN matrix can be regarded as vectors containing the regulated or the regulating information. The behavior similarity of two units can be described by the dot product of two regulated vectors or two regulating vectors. Biologists usually think the more similar two sequences are, the more likely they have similar behaviors. Whether the ratio of genes with similar behaviors is positively correlated with gene similarity is essential to our project. So we obtained 1.6 million sets of data by pairwise alignment of all the 1748 units in the GRN of K-12. Each set of data consists of gene similarity and behavior similarity. The result is analyzed and plotted in the figure. The linear fit shows that the ratio is positively correlated with the similarity.</p><br/><br />
<br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/2013/d/d0/USTC_Software_Simi-Ratio.png" /><br />
<p><strong>Figure 4.</strong>Linear fit of ratio-similarity relationship.</p></div><br />
<p align="justify">Although there are examples that a slight change in DNA sequence will significantly change the property of the gene, for example, sickle-cell disease, the influence is usually determined by the location and scale of the mutation. So the result is still convincing to some degree.</p><br />
<p><br />
<b>2.Filtering</b></p><br />
<h4>Random Noise</h4><br />
<p class="bodytext"></p><p align="justify">Normally, the similarity of two sequences will not be zero. Some computational<br />
experiments were carried out to study the random sequence similarities. We randomly<br />
chose a gene in the network and generated 1000 random sequences. The alignment result<br />
indicates that the random sequence similarities are Gauss distributed. The result suggests<br />
that some similarities are out of statistic significance.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/8/89/USTC_Software_Figure_4.png" /><br />
<p><strong>Figure 5.</strong> Random similarity distribution</p><br />
<h4>Filter</h4><br />
<p align="justify">We need the genes highly similar to the exogenous one to interact with it. The program will<br />
align the exogenous gene(query) with genes in the network(subject) and get the original<br />
similarities. In order to filter meaningless low values, a certain amount of random<br />
sequences are generated for each query-subject alignment. Normally, 100 is sufficient.<br />
Because the sequence length will influence alignment result, random sequences are fixed<br />
at the same length as the query one. Then align random sequences with the subject<br />
sequence. The statistic result of these random similarities is used as a threshold.<br /><br />
<div align="center">Threshold = μ + xσ</div><br /><br />
In the formula, μ is the average random similarity. σ is the standard deviation. x is used to<br />
control the filter determined by machine learning. If the original similarity is lower than the<br />
threshold, it is abandoned. It is usually means the original value is usually short of<br />
statistical significance.<br /><br /><br />
An example about filtring and consistency is presented in “Example”.<br />
</p><br />
<p><b>3.Regulation Calculation</b></p><br />
<p align="justify">If there is a three-unit network and they interact with each other as it is shown in the figure.<br />
The regulation is described by the GRN matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/8/8a/USTC_Software_Figure_5.png" /><br />
<p align="justify"><strong>Figure 6.</strong> Example network and its GRN matrix.</p></div><br />
<br />
<br />
<p align="justify">If D is the exogenous unit, we can obtain three similarity data sets of D with the units in the<br />
original GRN: <br />
<li style="margin-left:40px;">Promoter sequence similarity</li><br />
<li style="margin-left:40px;">Gene sequence similarity</li><br />
<li style="margin-left:40px;">Amino acid sequence similarity.</li><br />
<p><br />
The construction is equivalent to add a new column and a row into the original matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/9/97/USTC_Software_Figure_6.png" /><br />
<p><strong>Figure 7.</strong> Mathematical Equivalence</p></div><br />
<p align="justify">When filling the column, D is compared with the regulators of the unit in each row. The<br />
regulations in the row are consider separately and marked as “positive group” and<br />
“negative group”. The average similarity of each group represents the distance between<br />
the exogenous unit and the group. D is supposed to have the larger one's regulatory<br />
direction(positive or negative). The regulatory intensity is the weight average regulation of<br />
the chose group. The weight here is the amino acid sequence similarity.<br /><br /><br />
There are two conditions when fill the new row:<br /><br />
1. There are units having the same promoter as the exogenous unit.<br /><br />
2. There is no units having the same promoter as the exogenous unit.<br /><br /><br />
In condition 1, the units sharing the same promoter with the new member are picked out,<br />
and the following steps are the same as the construction of the column. The difference is<br />
the similarity used here is the gene sequence similarity. As explained in the regulation<br />
model part, the promoter is the main regulatory region, but the following sequence is also<br />
considered. Now the promoter is the same, so what we focus on are the gene sequences.<br /><br /><br />
In condition 2, the process is almost the same as constructing the new column. Promoter<br />
similarity is used because it is the main region.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/c/c5/USTC_Software_Figure_7.png" /><br />
<p><strong>Figure 8.</strong> Construct New GRN</p><br />
<h3>Clustering</h3><br />
<p><br />
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.</br></br><br />
For get a better regulation, we use online database DAVID to cluster all the genes in our whole GRN. Avoid of supersoftless, we hope to create an online communication with DAVID. After getting the cluster of our genes, we multiply the genes simalarity with a factor if they are in the same cluster.</br></br><br />
Though the source code of this part has already done, we lack the experiment information to set a propriate factor. All source code were pushed up to our github.<br />
</p><br />
</div><br />
</div><br />
<div class="jobs_trigger"><strong>Network Model</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Network analysis includes finding stable condition of network, adding new gene, finding new stable condition and changes from original condition to new condition. We use densities of materials to describe network condition. If all material densities are time-invariant, we can say the network condition is stable.</p><br />
<p class="bodytext"></p><p align="justify">Regulation relationship in genetic network includes positive regulation, negative regulation, positive-or-negative regulation and no regulation. We store regulation relationship in matrix R. Rji means the unit in line j and row i. For the material of original network, Rji=1 means material i enhance material j, Rji=-1 means material i repress material j, Rji=0 means material i has no influence on material j, Rji=2 means material i enhance or repress material j. For the new material, Rji ranges from -1 to 1. Rji<0 means the possibility of positive regulation is Rji; Rji>0 means the possibility of negative regulation is –Rji; Rji=0 means there is no regulation from i to j.<br />
We use Hill equations to describe intensity of regulation. Equations are like following:<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e0/USTC_Software_1.png" style="width:600px;"/><br />
<br/></br><br />
The left side of the equation is the derivative x(density) on t(time).”qi”,”pi”,”ri”,”mi”,”ni” are parameters, which determine the intensity of regulation."ri" is degradation rate. Mji is exponent. M is a matrix whose dimensions are equivalent to R's. Mji is 0 or ranges from 0.5 to 1.2 or ranges from -1.2 to 0.5. For the material of original network, if Rji=1,Mji ranges from 0.5 to 1.2;if Rji=-1, Mji ranges from -1.2 to -0.5; if Rji=2;Mji ranges from -1.2 to 0.5 or 0.5 to 1. These Mjis' absolute values are given randomly by program. If Rji=0, Mji=0. <br />
</br>For the new material,<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/64/USTC_Software_2.png"/><br />
<br/></br><br />
<br />
</p><br />
<p align="justify"><br />
Stable condition is the condition in which densities are time-invariant. We store material densities in a vector and solve the differential equations with Euler's formula, which is like below<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e6/USTC_Software_3.png" style="width:600px;"/><br />
<br/></br><br />
We know the network will be stable at last, so every material density has a limitation.<br />
<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Evaluate Network</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Record the original stable condition, set new material density to 0 and this is the new initial density vector. Solve new equations and record density vectors before the new condition is stable and store these data in a text file.</br></br><br />
<br />
To evaluate the new network, we introduce the grading system.<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/3/32/USTC_Software_4.png" style="width:600px;"/><br />
<img src="https://static.igem.org/mediawiki/2013/b/bc/USTC_Software_5.png" style="width:500px;"/><br />
<br />
<br/></br><br />
"xi" and "Xi" are densities of material i, which is not the new material."ny" is the number of materials. The more new densities are close to the original, the less the influence the cell endues. In general, cells close to the original cell are more likely to survive than those who are far different from the original cell. That is the thought of the grading system.</br></br><br />
We did a lot of running and found that the “AbsValue” ranges from 0 to 370, so "ScoreA" ranges from 0 to 4.9.We get the integer part and store it in an array, which has five sections. Generate 100 or 200 matrix M from matrix R and run the original and new network for each M, so we can get 100 or 200 of "ScoreA"s. The section which has maximum "ScoreA"s is the eventual score.<br />
</p> <br />
</div><br />
<br />
<br />
<h2>Reverse Analysis</h2><br />
<div class="jobs_trigger"><strong>Virtual Gene</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Expression Range</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Before prediction, the expression of specific genes which the experimenter needs should be input into our software as well as the improvement or depression. The number of target gene is SIX at most.</br></br><br />
It is a must that figuring out the strongest and weakest expression strength before inputting the extreme cases into the target expression. The way to find out the strongest and weakest expression is modeling the GRN's steady state by a large amount of random regulation from -1 and 1. We ran it for 1000 times to get the range of gene expression. On the other hand, the expression of genes unpicked by the users should be stable as much as possible. The initial strength of expression is calculated by modeling the original GRN with Hill's equation.<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Particle Swarm Optimaztion</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify"><br />
For getting the best regulation, our software uses PSO algorithm based on 30 particles to simulate the GRN's changing. First of all, the interactions of regulator and regulated-by have been put into those particles in random so that each particle will have the whole set of regulation. Secondly, the variance between target expressions and stable expression of new GRN have been regarded as the optimize requirements in PSO algorithm. As a result, the minimal variance of 30 particles is the global optimum and the minimal variance of the procession in one particle is the local optimum. Then, taking a step towards global and local optimum as well as considering the inertia and perturbation avoids falling into the sub-optimal condition.</br></br><br />
At last, when the variance of expression reaches an acceptable range, our software picks out and saves the best global optimum particle following by the movement of those particles stop.</br></br><br />
We constantly revises the factors in PSO algorithm by machine learning method for accurate simulation with a fast PSO particle-motion equation. At the same time, our software also filter the result of regulatory value which is more intuitive.<br />
</p><br />
<br />
<br />
<br />
</div><br />
<div class="jobs_trigger"><strong>Locate Optimal Target</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">To improve the efficiency of choosing a suitable gene after getting a series of regulatory value, our software picks out some obvious regulation. The value of regulation is between -1 to 1 in which -1 means negative effect and 1 means positive effect. As a result, what our software has done is filtering out the absolute value which is lower than 0.9. Because the difference of regulatory intensity lower than 0.1 has very little effect to the stable expression, the final result of regulation is indicated by Boolean value.</br></br><br />
The format of regulatory prediction in “Result”:</br><br />
Gene_name->Gene_name regulation(+/-)<br />
<br />
</p> <br />
<br />
<br />
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</div><br />
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<h1 align="justify">Methodologies</h1><br />
<p align="justify">In order to simulate the GRN's working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</br><br/><br />
There are four parts of methodologies: Database, Operon Theory and Regulatory Model, Forward Analysis and Reverse Analysis.<br />
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<h2>Database</h2><br />
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<div class="jobs_trigger"><strong>Abstract</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">To simulate and analyze a genetic regulatory network (GRN), we need to build an objects' array to store the complete information of each gene. It contains regulation relationships between genes, sequences of genes, sequences of promoters and so on. However, it's hard to find an appropriate database online containing all information we need in a simple file. RegulonDB has downloadable files about the regulation between transcription factors (TF) and genes. Files about genetic information, transcription unit information and promoter information can also be downloaded from the RegulonDB. All those files have been put into file “source data” in the root directory of our software. They contain all information the simulation needs and we use fetching module to achieve data extraction and integration. There are four steps: fetch regulation relationships, fetch gene information, fetch promoter information and integrate information above.<br />
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<div class="jobs_trigger"><strong>Fetch Regulation</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">In GRN, there are two kinds of files: <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_tf.txt"> TF to TF</a> and <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_gene.txt">TF to Gene</a>. Since the database about the regulation between TFs and Genes contains only one-way interaction, the matrix of GRN is a rectangle.</br></br><br />
First of all, read the regulation relationship of TFs. Our software filters the documentation of RegulonDB on the head of all files and then reads the name of regulate and regulated TF, which is also the name of its genes, one by one. In the same time, our software numerates the genes and stores their names into an objects' array of genetic data. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/69/USTC_Software_TT.jpg"/><br />
The regulation of TFs has been put into a square matrix whose row is the regulator and column is the one regulated by. To make our GRN as complete as possible, the regulation between TF and genes has joined into the matrix. The one-way interaction results that we must read the TF in order to fulfill the regulator before completing the TF to gene's regulation in the same way of TF to TF. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/47/USTC_Software_TG.jpg"/><br />
At last, a regulatory matrix whose row represents regulate gene (TF) and whose column represents gene regulated by (TF+Gene) has been output into a file called “old_GRN” in root directory. The values in GRN matrix are regulations in which “1” means positive activation, “-1” means repression and “0” means no relationship. There have been some regulations both positive and negative identified regulations are determined by the experimental environment. As a result, our software picks out those uncertain genes and stores them into a file named “uncertain_database”.</br></br><br />
&nbsp;&nbsp;The format of uncertain database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;? &nbsp;&nbsp;&nbsp;Gene_name->Gene_name</br></br><br />
<br />
The question mark represents the unknown regulation between regulator and regulated-by whose names presented afterward. Users could replace the question mark with the data known in past experiment. (“+” rep positive, “-” rep negative). Our software will replace the values in matrix automatically. But if not rewrote, our software will regard those regulation as unknown.<br />
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<div class="jobs_trigger"><strong> Fetch Gene Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify"><br />
All gene information has been deposited into a file named gene_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/Gene_sequence.txt">here</a>. In order of picking out the genes in GRN as fast as possible, all genetic information are stored in a “map”. “Map” is just like a dictionary yet its words are names of genes and its descriptions of words are replaced by genetic information. By using binary tree method, it is very fast to search the “word” wanted in the “dictionary”. As tested, the speed of binary tree method built-in “map” function is 720 times faster than traversal method.</br></br><br />
&nbsp;&nbsp;The format of Gene Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;Left_end_position &nbsp;&nbsp;&nbsp;Right_end_position &nbsp;&nbsp;&nbsp;DNA_strand &nbsp;&nbsp;&nbsp;Product_type &nbsp;&nbsp;&nbsp;&nbsp;Product_name &nbsp;&nbsp;&nbsp;Start_codon_sequence&nbsp;&nbsp;&nbsp; Stop_codon_sequence &nbsp;&nbsp;&nbsp;Gene_sequence</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/4/45/USTC_Software_GI.jpg"/><br />
The label of the map vector is gene name which will be picked out based on the names read in regulation matrix before. It is really fast using the binary tree method to find the specific genetic information and store them into a specific object. Those information includes gene ID, left position, right position, gene description and gene sequence. The gene ID is used to link to RegulonDB's gene details; The left position is used to find its specific transcription unit; The right position is used to figure out the base amount; The description of genes is used to distinguish the RNA and protein; The sequence is used to predict the regulation by alignment.<br />
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<div class="jobs_trigger"> <strong>Fetch Promoter Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">All promoter information has been deposited into a file named promoter_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/PromoterSet.txt">here</a>. But we also need transcription unit information because the information files about promoter do not contain all genes' names backward. “TU Info” file, which can be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/TUSet.txt">here</a>, contains the starting position of each TU and its promoter name. Our software picks out the starting position into a integer array. Using the left position picked out in gene info, our software would find out which unit the gene belongs to through dichotomy method and then stores the name of promoter into corresponding object.</br></br><br />
&nbsp;&nbsp;The format of TU info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Operon_name &nbsp;&nbsp;&nbsp;Unit_name &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;Transcription_start_site ......</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/1/1e/USTC_Software_TI.jpg"/><br />
The principle of fetching information of promoters is same as fetching genes's. Our software stores the promoter information from the file named “promoter_info” in a “map” which could be used to pick out the promoter sequence by searching promoter name through binary tree method.</br></br><br />
&nbsp;&nbsp;The format of Promoter Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Promoter_ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Promoter_name</br></br><br />
<img src="https://static.igem.org/mediawiki/2013/8/8a/USTC_Software_PI.jpg"/><br />
The sequence of promoter will be used in the alignment method in next module which could make a prediction of exogenous genes' regulation pattern.<br />
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<div class="jobs_trigger"> <strong>Integration</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify"> <br />
Our software integrates all information we picked out about genes and generates a file named “all_info” —— all information about genes —— for the output graphical interface's reading. In the meanwhile, the array of objects containing all information has been stored in computer memory which greatly improve the computing speed of our software.</br></br><br />
&nbsp;&nbsp;The format of all_info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;No. &nbsp;&nbsp;&nbsp;promoter_sequence &nbsp;&nbsp;&nbsp;gene_sequence &nbsp;&nbsp;&nbsp;gene_name &nbsp;&nbsp;&nbsp;ID &nbsp;&nbsp;&nbsp;left_position &nbsp;&nbsp;&nbsp;right_position &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;&nbsp;description</br><br />
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The fetching module generates three files: old_GRN, all_info and uncertain_database.</br><br />
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<h2>Operon Theory and Regulatory Model</h2><br />
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<div class="jobs_trigger"><strong>Operon Theory</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><br />
<p align="justify">In genetics, an operon is a functioning unit of genomic DNA containing a cluster of genes<br />
under the control of a single regulatory signal or promoter. The genes contained in the<br />
operon are either expressed together or not at all. Several genes must be both cotranscribed<br />
and co-regulated to define an operon.<br /><br /><br />
The first time "operon" was proposed is in a paper of French Academic Science, 1960.<br />
The lac operon of the model bacterium E. coli was discovered and provides a typical<br />
example of operon function. It consists a promoter, an operator, three structural genes and<br />
a terminator. The operon is regulated by several factors including the availability of glucose<br />
and lactose.<br /><br /><br />
From this paper, the so-called general theory of the operon was developed. According to<br />
the theory, all genes are controlled by means of operons through a single feedback<br />
regulatory mechanism-repression. The first operon to be described was the lac operon in<br />
E. coli. The 1965 Nobel Prize in Physiology and Medicine was awarded to François Jacob,<br />
André Michel Lwoff and Jacques Lucien Monod for their discoveries concerning the operon and virus synthesis.<br /><br />
</p><br />
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<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/7/7d/USTC_Software_Figure_1.png" /><br />
<p align="center"><strong>Figure 1.</strong> Structure of Operon</p></div><br />
<p align="justify">An operon is made up of several structural genes arranged under a common promoter and<br />
regulated by a common operator. It is defined as a set of adjacent structural genes, plus<br />
the adjacent regulatory signals that affect transcription of the structural genes. The<br />
regulators of a given operon, including repressors, corepressors and activators, are not<br />
necessarily coded for by that operon.<br /><br /><br />
As a unit of transcription, upstream of the structural genes lies a promoter sequence which<br />
provides a site for RNA polymerase to bind and initiate transcription. Close to the promoter<br />
lies a section of DNA called an operator.<br /><br /><br />
Operon regulation can be either negative or positive by induction or repression. Negative<br />
control involves the binding of a repressor to the operator to prevent transcription.<br />
Operons can also be positively controlled. An activator protein binds to DNA, usually at a<br />
site other than the operator, to stimulate transcription.<br />
</p><br />
<div align="center"><img style="width:600px;" src="https://static.igem.org/mediawiki/igem.org/2/25/USTC_Software_Figure_2.png"/><br />
<p align="justify"><strong>Figure 2.</strong> Regulation of Operon<br />
1: RNA Polymerase, 2: Repressor, 3: Promoter, 4: Operator, 5: Lactose, 6: lacZ, 7:<br />
lacY, 8: lacA. Top: The gene is essentially turned off. There is no lactose to inhibit the<br />
repressor, so the repressor binds to the operator, which obstructs the RNA polymerase<br />
from binding to the promoter and making lactase.Bottom: The gene is turned on.Lactose<br />
is inhibiting the repressor, allowing the RNA polymerase to bind with the promoter, and<br />
express the genes, which synthesize lactase. Eventually, the lactase will digest all of the<br />
lactose, until there is none to bind to the repressor. The repressor will then bind to the<br />
operator, stopping the manufacture of lactase.</p></div><br />
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<div class="jobs_trigger"><strong>Regulatory Model</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">Regulation of gene expression includes four levels. We choose the transcriptional level to simulate the regulation both for its significance and model simplification.</p><br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/igem.org/8/87/USTC_Software_Figure_3.png" /><br />
<p><strong>Figure 3.</strong>Regulation of gene expression.<br />Our regulation model is built based on the operon theory.<br /> The promoter region is regarded as the main regulatory region.</p></div><br />
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<div class="jobs_trigger"> <strong>Similarity and Homology</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment. It is defined as the proportion of the common subsequence in the aligned sequence. Any two sequences share a certain<br />
similarity. It should be noted that similarity and homology are two different concepts.<br /><br /><br />
As with anatomical structures, homology between protein or DNA sequences is defined in<br />
terms of shared ancestry. Two segments of DNA can have shared ancestry because of<br />
either a speciation event or a duplication event. The terms “percent homology” and<br />
“sequence similarity” are often used interchangeably. As with anatomical structures, high<br />
sequence similarity might occur because of convergent evolution, or, as with shorter<br />
sequences, because of chance. Such sequences are similar but not homologous.<br />
Sequence regions that homologous are also called conserved.<br /><br /><br />
In our project, we use similarity to connect the exogenous gene with the original network.<br />
Because there is a good chance that the exogenous gene is not homologous with the<br />
genes in the network.</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The GRN matrix is the mathematical description of gene regulatory network in which “1” represents “enhance”, “-1” represents “repress” and “0” represents “no regulatory relationship”. The units(RU) in x-axis regulate the units in y-axis. A row can be seen as a vector containing all the information of the target(corresponding unit in the y-axis). Similarly, a column can be seen as a vector containing all the information of the regulator(corresponding unit in the x-axis).</p> <br />
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<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment based on Needleman-Wunsch algorithm[FIXME: wiki link here]. The Needleman-Wunsch algorithm performs a global alignment on two protein sequences or nucleotide sequences. It was the first application of dynamic programming to biological sequence comparison.<br /><br /><br />
<br />
When dynamic programming is applicable, the method takes far less time than naive methods. Using a naive method, many of the subproblems are generated and sovled many times. The dynamic programming approach seeks to solve each subproblem only once. Once the solution to a given subproblem has been computed, it is stored to be looked up next time.<br /><br /><br />
<br />
Like the Needleman-Wunsch algorithm, of which it is a variation, Smith-Waterman is also a dynamic programming algorithm. But it is a local sequence alignment algorithm. The famous BLAST(Basic Local Alignment Search Tool) is improved from Smith-Waterman algorithm. Although local algorithm has the desirable property that it is guaranteed to find the optimal local alignment, we decided to choose the global one because we regarded the segment sequence as a unit.<br /><br /><br />
<br />
Sequences are aligned with different detailed methods in different situations. In the regulated side, what we care about is the DNA sequence. In the regulating side, it is the amino acid sequence. When it comes to predict the regulated behavior, we use a DNA substitution matrix to align promoter and protein coding sequences. In the prediction of regulating behavior, the substitution matrix BLOSUM_50 is used to align the amino acid sequences translated from protein coding sequences.<br /><br /><br />
<br />
The promoter similarities of the query unit and subject units are stored in a vector. The protein coding similarities are stored in another vector. These vectors are prepared to be used in the new network construction.<br />
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<h2>Forward Analysis</h2><br />
<div class="jobs_trigger"><strong>Construct New GRN</strong></div><br />
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<h3>User Input</h3><br />
<p align="justify"><br />
Some genes' regulation could be get from experiment. So, if users could get the unknow regulation between new gene and old ones, they could manually set the interactions which do not need model. Those regulations will be used in later simulation.<br />
</p><br />
<h3>Simalarity Analysis</h3><br />
<p align="justify"><b>1.Sequence</b></br><br />
<h4>Needleman-Wunsch Algorithm</h4><br />
The Needleman-Wunsch algorithm was first published in1970 by Saul B. Needleman and Christian D. Wunsch. It performs a global alignment of two sequences and is mostly used in bioinformatics to align protein or nucleotide sequence. Our software applied this algorithm in the alignment of DNA and amino acid sequences.<br/><br/><br />
<br />
The Needleman-Wunsch algorithm is one kind of dynamic programming and It was the first attempt in biological sequence comparison of dynamic programming.<br/><br/><br />
<br />
Here is an example of Needleman-Wunsch algorithm. S(a,b) is the similarity of character a and character b. The scores of characters are shown in the similarity matrix. We assume this matrix was<br />
</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/5/52/USTC_Software_DNA_S_M.png"/></div> <br />
<p>And we uses linear gap penalty, denoted by d, here, we set the gap penalty as -5.Then the alignment:</p><br />
<p align="center"><strong><em><br />
A: AGACTAGTTAC<br/><br />
B: CGA - - - GACGT<br />
</em></strong></p><br />
<br />
<p>would have the following score:</p><br />
<p align="center"><strong><em><br />
S(A,C)+S(G,C)+S(A,A)+(3)+S(G,G)+S(T,A)+S(T,C)+S(A,G)+S(C,T) = -3+7+10-(3x5)+7+(-4)+0+(-1)+0 = 1<br />
</em></strong></p><br />
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<p align="justify">To find the highest score of alignment, in this algorithm, a two dimensional matrix F with sequences and scores was allocated. The score in row i, column j is denoted by Fij. There is one column for each character in sequence A and one row for each character in sequence B. Therefore, if we align sequences with sizes of n and m, the amount of memory taken up here is O(n,m).<br/><br/><br />
<br />
As the algorithm going on, Fij was calculated to be the optimal score by the principle as following:<br/><br />
Basis:<br />
</p><br />
<p align="center"><strong><em>Fi0 = d*i<br/>F0j = d*j</em></strong></p><br />
<p>Recursion:</p><br />
<p align="center"><strong><em>Fij = max(F(i-1,j-1) + S(Ai,Bj), F(i-1,j) + d, F(i,j-1) + d)</em></strong></p><br/><br />
<p>The pseudo-code of this algorithm would look like this:</p><br />
<br/><br />
<div id="pseudo"><p><br />
<strong> for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; F(i,0) <-- d*i<br/><br />
<strong> for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; F(0,j) <-- d*j<br/><br />
<strong>for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; <strong>for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; {<br/><br />
&nbsp; &nbsp; Match <-- F(i-1,j-1) + S(Ai,Bj)<br/><br />
&nbsp; &nbsp; Delete <-- F(i-1,j) + d<br/><br />
&nbsp; &nbsp; Insert <-- F(i,j-1) + d<br/><br />
&nbsp; &nbsp; F(i,j) <-- <strong>max</strong>(Match, Insert, Delete)<br/><br />
&nbsp; }<br />
</p><br />
</div><br />
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<p align="justify">After the matrix F was computed, Fnm would be the maximum score among all possible alignment.<br/><br/><br />
<br />
If you want to see the optimal alignment, you can trace back from Fnm by comparing three possible sources mentioned in the above code (Match, Insert and Delete). If Match, then Aj and Bi are aligned, if Insert, Bi was aligned with a gap and if Delete, then Aj and a gap are aligned. Also, you may find there are not only one optimal alignment.<br/><br/><br />
As for the example, we would get the following matrix by applying Needleman Wunsch algorithm:</p><br />
<br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/e/e2/USTC_Software_arrow_game.png"/></div><br />
<p>And the optimal alignment would be:</p><br />
<br />
<p align="center"><strong><em>- - AGACTAGTTAC <br/><br />
CGAGAC - - GT - - -<br />
</em></strong></p><br />
<h4>A Supplementary Game</h4><br />
<p align="justify">The rows and columns in the GRN matrix can be regarded as vectors containing the regulated or the regulating information. The behavior similarity of two units can be described by the dot product of two regulated vectors or two regulating vectors. Biologists usually think the more similar two sequences are, the more likely they have similar behaviors. Whether the ratio of genes with similar behaviors is positively correlated with gene similarity is essential to our project. So we obtained 1.6 million sets of data by pairwise alignment of all the 1748 units in the GRN of K-12. Each set of data consists of gene similarity and behavior similarity. The result is analyzed and plotted in the figure. The linear fit shows that the ratio is positively correlated with the similarity.</p><br/><br />
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<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/2013/d/d0/USTC_Software_Simi-Ratio.png" /><br />
<p><strong>Figure 4.</strong>Linear fit of ratio-similarity relationship.</p></div><br />
<p align="justify">Although there are examples that a slight change in DNA sequence will significantly change the property of the gene, for example, sickle-cell disease, the influence is usually determined by the location and scale of the mutation. So the result is still convincing to some degree.</p><br />
<p><br />
<b>2.Filtering</b></p><br />
<h4>Random Noise</h4><br />
<p class="bodytext"></p><p align="justify">Normally, the similarity of two sequences will not be zero. Some computational<br />
experiments were carried out to study the random sequence similarities. We randomly<br />
chose a gene in the network and generated 1000 random sequences. The alignment result<br />
indicates that the random sequence similarities are Gauss distributed. The result suggests<br />
that some similarities are out of statistic significance.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/8/89/USTC_Software_Figure_4.png" /><br />
<p><strong>Figure 5.</strong> Random similarity distribution</p><br />
<h4>Filter</h4><br />
<p align="justify">We need the genes highly similar to the exogenous one to interact with it. The program will<br />
align the exogenous gene(query) with genes in the network(subject) and get the original<br />
similarities. In order to filter meaningless low values, a certain amount of random<br />
sequences are generated for each query-subject alignment. Normally, 100 is sufficient.<br />
Because the sequence length will influence alignment result, random sequences are fixed<br />
at the same length as the query one. Then align random sequences with the subject<br />
sequence. The statistic result of these random similarities is used as a threshold.<br /><br />
<div align="center">Threshold = μ + xσ</div><br /><br />
In the formula, μ is the average random similarity. σ is the standard deviation. x is used to<br />
control the filter determined by machine learning. If the original similarity is lower than the<br />
threshold, it is abandoned. It is usually means the original value is usually short of<br />
statistical significance.<br /><br /><br />
An example about filtring and consistency is presented in “Example”.<br />
</p><br />
<p><b>3.Regulation Calculation</b></p><br />
<p align="justify">If there is a three-unit network and they interact with each other as it is shown in the figure.<br />
The regulation is described by the GRN matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/8/8a/USTC_Software_Figure_5.png" /><br />
<p align="justify"><strong>Figure 6.</strong> Example network and its GRN matrix.</p></div><br />
<br />
<br />
<p align="justify">If D is the exogenous unit, we can obtain three similarity data sets of D with the units in the<br />
original GRN: <br />
<li style="margin-left:40px;">Promoter sequence similarity</li><br />
<li style="margin-left:40px;">Gene sequence similarity</li><br />
<li style="margin-left:40px;">Amino acid sequence similarity.</li><br />
<p><br />
The construction is equivalent to add a new column and a row into the original matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/9/97/USTC_Software_Figure_6.png" /><br />
<p><strong>Figure 7.</strong> Mathematical Equivalence</p></div><br />
<p align="justify">When filling the column, D is compared with the regulators of the unit in each row. The<br />
regulations in the row are consider separately and marked as “positive group” and<br />
“negative group”. The average similarity of each group represents the distance between<br />
the exogenous unit and the group. D is supposed to have the larger one's regulatory<br />
direction(positive or negative). The regulatory intensity is the weight average regulation of<br />
the chose group. The weight here is the amino acid sequence similarity.<br /><br /><br />
There are two conditions when fill the new row:<br /><br />
1. There are units having the same promoter as the exogenous unit.<br /><br />
2. There is no units having the same promoter as the exogenous unit.<br /><br /><br />
In condition 1, the units sharing the same promoter with the new member are picked out,<br />
and the following steps are the same as the construction of the column. The difference is<br />
the similarity used here is the gene sequence similarity. As explained in the regulation<br />
model part, the promoter is the main regulatory region, but the following sequence is also<br />
considered. Now the promoter is the same, so what we focus on are the gene sequences.<br /><br /><br />
In condition 2, the process is almost the same as constructing the new column. Promoter<br />
similarity is used because it is the main region.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/c/c5/USTC_Software_Figure_7.png" /><br />
<p><strong>Figure 8.</strong> Construct New GRN</p><br />
<h3>Clustering</h3><br />
<p><br />
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.</br></br><br />
For get a better regulation, we use online database DAVID to cluster all the genes in our whole GRN. Avoid of supersoftless, we hope to create an online communication with DAVID. After getting the cluster of our genes, we multiply the genes simalarity with a factor if they are in the same cluster.</br></br><br />
Though the source code of this part has already done, we lack the experiment information to set a propriate factor. All source code were pushed up to our github.<br />
</p><br />
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<div class="jobs_trigger"><strong>Network Model</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Network analysis includes finding stable condition of network, adding new gene, finding new stable condition and changes from original condition to new condition. We use densities of materials to describe network condition. If all material densities are time-invariant, we can say the network condition is stable.</p><br />
<p class="bodytext"></p><p align="justify">Regulation relationship in genetic network includes positive regulation, negative regulation, positive-or-negative regulation and no regulation. We store regulation relationship in matrix R. Rji means the unit in line j and row i. For the material of original network, Rji=1 means material i enhance material j, Rji=-1 means material i repress material j, Rji=0 means material i has no influence on material j, Rji=2 means material i enhance or repress material j. For the new material, Rji ranges from -1 to 1. Rji<0 means the possibility of positive regulation is Rji; Rji>0 means the possibility of negative regulation is –Rji; Rji=0 means there is no regulation from i to j.<br />
We use Hill equations to describe intensity of regulation. Equations are like following:<br />
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<img src="https://static.igem.org/mediawiki/2013/e/e0/USTC_Software_1.png" style="width:600px;"/><br />
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The left side of the equation is the derivative x(density) on t(time).”qi”,”pi”,”ri”,”mi”,”ni” are parameters, which determine the intensity of regulation."ri" is degradation rate. Mji is exponent. M is a matrix whose dimensions are equivalent to R's. Mji is 0 or ranges from 0.5 to 1.2 or ranges from -1.2 to 0.5. For the material of original network, if Rji=1,Mji ranges from 0.5 to 1.2;if Rji=-1, Mji ranges from -1.2 to -0.5; if Rji=2;Mji ranges from -1.2 to 0.5 or 0.5 to 1. These Mjis' absolute values are given randomly by program. If Rji=0, Mji=0. <br />
</br>For the new material,<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/64/USTC_Software_2.png"/><br />
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<p align="justify"><br />
Stable condition is the condition in which densities are time-invariant. We store material densities in a vector and solve the differential equations with Euler's formula, which is like below<br />
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<img src="https://static.igem.org/mediawiki/2013/e/e6/USTC_Software_3.png" style="width:600px;"/><br />
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We know the network will be stable at last, so every material density has a limitation.<br />
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<div class="jobs_trigger"><strong>Evaluate Network</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Record the original stable condition, set new material density to 0 and this is the new initial density vector. Solve new equations and record density vectors before the new condition is stable and store these data in a text file.</br></br><br />
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To evaluate the new network, we introduce the grading system.<br />
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<img src="https://static.igem.org/mediawiki/2013/3/32/USTC_Software_4.png" style="width:600px;"/><br />
<img src="https://static.igem.org/mediawiki/2013/b/bc/USTC_Software_5.png" style="width:500px;"/><br />
<br />
<br/></br><br />
"xi" and "Xi" are densities of material i, which is not the new material."ny" is the number of materials. The more new densities are close to the original, the less the influence the cell endues. In general, cells close to the original cell are more likely to survive than those who are far different from the original cell. That is the thought of the grading system.</br></br><br />
We did a lot of running and found that the “AbsValue” ranges from 0 to 370, so "ScoreA" ranges from 0 to 4.9.We get the integer part and store it in an array, which has five sections. Generate 100 or 200 matrix M from matrix R and run the original and new network for each M, so we can get 100 or 200 of "ScoreA"s. The section which has maximum "ScoreA"s is the eventual score.<br />
</p> <br />
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<h2>Reverse Analysis</h2><br />
<div class="jobs_trigger"><strong>Virtual Gene</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
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<div class="jobs_trigger"><strong>Expression Range</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">Before prediction, the expression of specific genes which the experimenter needs should be input into our software as well as the improvement or depression. The number of target gene is SIX at most.</br></br><br />
It is a must that figuring out the strongest and weakest expression strength before inputting the extreme cases into the target expression. The way to find out the strongest and weakest expression is modeling the GRN's steady state by a large amount of random regulation from -1 and 1. We ran it for 1000 times to get the range of gene expression. On the other hand, the expression of genes unpicked by the users should be stable as much as possible. The initial strength of expression is calculated by modeling the original GRN with Hill's equation.<br />
</p><br />
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<div class="jobs_trigger"><strong>Particle Swarm Optimaztion</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify"><br />
For getting the best regulation, our software uses PSO algorithm based on 30 particles to simulate the GRN's changing. First of all, the interactions of regulator and regulated-by have been put into those particles in random so that each particle will have the whole set of regulation. Secondly, the variance between target expressions and stable expression of new GRN have been regarded as the optimize requirements in PSO algorithm. As a result, the minimal variance of 30 particles is the global optimum and the minimal variance of the procession in one particle is the local optimum. Then, taking a step towards global and local optimum as well as considering the inertia and perturbation avoids falling into the sub-optimal condition.</br></br><br />
At last, when the variance of expression reaches an acceptable range, our software picks out and saves the best global optimum particle following by the movement of those particles stop.</br></br><br />
We constantly revises the factors in PSO algorithm by machine learning method for accurate simulation with a fast PSO particle-motion equation. At the same time, our software also filter the result of regulatory value which is more intuitive.<br />
</p><br />
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<div class="jobs_trigger"><strong>Locate Optimal Target</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<p align="justify">To improve the efficiency of choosing a suitable gene after getting a series of regulatory value, our software picks out some obvious regulation. The value of regulation is between -1 to 1 in which -1 means negative effect and 1 means positive effect. As a result, what our software has done is filtering out the absolute value which is lower than 0.9. Because the difference of regulatory intensity lower than 0.1 has very little effect to the stable expression, the final result of regulation is indicated by Boolean value.</br></br><br />
The format of regulatory prediction in “Result”:</br><br />
Gene_name->Gene_name regulation(+/-)<br />
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</p> <br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/MethodTeam:USTC-Software/Project/Method2013-10-27T12:03:32Z<p>USTCkun: </p>
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<h1 align="justify">Methodologies</h1><br />
<p align="justify">In order to simulate the GRN's working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</br><br/><br />
There are five parts of methodologies: Fetch Database, Alignment Analyze, New Network Construction, Network Model and Predict.<br />
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<h2>Fetch Database</h2><br />
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<div class="jobs_trigger"><strong>Fetch Database Abstract</strong></div><br />
<div class="jobs_item" style="display: none;"><br />
<div class="jobs_trigger"><strong>Fetch Database Abstract</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">To simulate and analyze a genetic regulatory network (GRN), we need to build an objects' array to store the complete information of each gene. It contains regulation relationships between genes, sequences of genes, sequences of promoters and so on. However, it's hard to find an appropriate database online containing all information we need in a simple file. RegulonDB has downloadable files about the regulation between transcription factors (TF) and genes. Files about genetic information, transcription unit information and promoter information can also be downloaded from the RegulonDB. All those files have been put into file “source data” in the root directory of our software. They contain all information the simulation needs and we use fetching module to achieve data extraction and integration. There are four steps: fetch regulation relationships, fetch gene information, fetch promoter information and integrate information above.<br />
</p><br />
</div><p class="bodytext"></p><p align="justify">To simulate and analyze a genetic regulatory network (GRN), we need to build an objects' array to store the complete information of each gene. It contains regulation relationships between genes, sequences of genes, sequences of promoters and so on. However, it's hard to find an appropriate database online containing all information we need in a simple file. RegulonDB has downloadable files about the regulation between transcription factors (TF) and genes. Files about genetic information, transcription unit information and promoter information can also be downloaded from the RegulonDB. All those files have been put into file “source data” in the root directory of our software. They contain all information the simulation needs and we use fetching module to achieve data extraction and integration. There are four steps: fetch regulation relationships, fetch gene information, fetch promoter information and integrate information above.<br />
</p><br />
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<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Fetch Regulation</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">In GRN, there are two kinds of files: <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_tf.txt">TF to TF</a> and <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_gene.txt">TF to Gene</a>. Since the database about the regulation between TFs and Genes contains only one-way interaction, the matrix of GRN is a rectangle.</br></br><br />
First of all, read the regulation relationship of TFs. Our software filters the documentation of RegulonDB on the head of all files and then reads the name of regulate and regulated TF, which is also the name of its genes, one by one. In the same time, our software numerates the genes and stores their names into an objects' array of genetic data. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<br />
The regulation of TFs has been put into a square matrix whose row is the regulator and column is the one regulated by. To make our GRN as complete as possible, the regulation between TF and genes has joined into the matrix. The one-way interaction results that we must read the TF in order to fulfill the regulator before completing the TF to gene's regulation in the same way of TF to TF. </br></br><br />
&nbsp;&nbsp;The format of regulation database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;TF_name &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;+/-/+-</br></br><br />
<br />
At last, a regulatory matrix whose row represents regulate gene (TF) and whose column represents gene regulated by (TF+Gene) has been output into a file called “old_GRN” in root directory. The values in GRN matrix are regulations in which “1” means positive activation, “-1” means repression and “0” means no relationship. There have been some regulations both positive and negative identified regulations are determined by the experimental environment. As a result, our software picks out those uncertain genes and stores them into a file named “uncertain_database”.</br></br><br />
&nbsp;&nbsp;The format of uncertain database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;? &nbsp;&nbsp;&nbsp;Gene_name->Gene_name</br></br><br />
<br />
The question mark represents the unknown regulation between regulator and regulated-by whose names presented afterward. Users could replace the question mark with the data known in past experiment. (“+” rep positive, “-” rep negative). Our software will replace the values in matrix automatically. But if not rewrote, our software will regard those regulation as unknown.<br />
</p><br />
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<div class="jobs_trigger"><strong> Fetch Gene Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify"><br />
All gene information has been deposited into a file named gene_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/Gene_sequence.txt">here</a>. In order of picking out the genes in GRN as fast as possible, all genetic information are stored in a “map”. “Map” is just like a dictionary yet its words are names of genes and its descriptions of words are replaced by genetic information. By using binary tree method, it is very fast to search the “word” wanted in the “dictionary”. As tested, the speed of binary tree method built-in “map” function is 720 times faster than traversal method.</br></br><br />
&nbsp;&nbsp;The format of Gene Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Gene_name &nbsp;&nbsp;&nbsp;Left_end_position &nbsp;&nbsp;&nbsp;Right_end_position &nbsp;&nbsp;&nbsp;DNA_strand &nbsp;&nbsp;&nbsp;Product_type &nbsp;&nbsp;&nbsp;&nbsp;Product_name &nbsp;&nbsp;&nbsp;Start_codon_sequence&nbsp;&nbsp;&nbsp; Stop_codon_sequence &nbsp;&nbsp;&nbsp;Gene_sequence</br></br><br />
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The label of the map vector is gene name which will be picked out based on the names read in regulation matrix before. It is really fast using the binary tree method to find the specific genetic information and store them into a specific object. Those information includes gene ID, left position, right position, gene description and gene sequence. The gene ID is used to link to RegulonDB's gene details; The left position is used to find its specific transcription unit; The right position is used to figure out the base amount; The description of genes is used to distinguish the RNA and protein; The sequence is used to predict the regulation by alignment.<br />
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</p><br />
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<div class="jobs_trigger"> <strong>Fetch Promoter Info</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">All promoter information has been deposited into a file named promoter_info which could be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/PromoterSet.txt">here</a>. But we also need transcription unit information because the information files about promoter do not contain all genes' names backward. “TU Info” file, which can be downloaded <a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/files/TUSet.txt">here</a>, contains the starting position of each TU and its promoter name. Our software picks out the starting position into a integer array. Using the left position picked out in gene info, our software would find out which unit the gene belongs to through dichotomy method and then stores the name of promoter into corresponding object.</br></br><br />
&nbsp;&nbsp;The format of TU info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Operon_name &nbsp;&nbsp;&nbsp;Unit_name &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;Transcription_start_site ......</br></br><br />
<br />
The principle of fetching information of promoters is same as fetching genes's. Our software stores the promoter information from the file named “promoter_info” in a “map” which could be used to pick out the promoter sequence by searching promoter name through binary tree method.</br></br><br />
&nbsp;&nbsp;The format of Promoter Info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;Promoter_ID_assigned_by_RegulonDB &nbsp;&nbsp;&nbsp;Promoter_name</br></br><br />
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The sequence of promoter will be used in the alignment method in next module which could make a prediction of exogenous genes' regulation pattern.<br />
</p> </div> <br />
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<div class="jobs_trigger"> <strong>Integration</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify"> <br />
Our software integrates all information we picked out about genes and generates a file named “all_info” —— all information about genes —— for the output graphical interface's reading. In the meanwhile, the array of objects containing all information has been stored in computer memory which greatly improve the computing speed of our software.</br></br><br />
&nbsp;&nbsp;The format of all_info database:</br><br />
&nbsp;&nbsp;&nbsp;&nbsp;No. &nbsp;&nbsp;&nbsp;promoter_sequence &nbsp;&nbsp;&nbsp;gene_sequence &nbsp;&nbsp;&nbsp;gene_name &nbsp;&nbsp;&nbsp;ID &nbsp;&nbsp;&nbsp;left_position &nbsp;&nbsp;&nbsp;right_position &nbsp;&nbsp;&nbsp;promoter_name &nbsp;&nbsp;&nbsp;&nbsp;description</br><br />
<br />
The fetching module generates three files: old_GRN, all_info and uncertain_database.</br><br />
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<div id="Alignment_Analyze"><br />
<h2>Operon Theory and Regulatory Model</h2><br />
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<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Operon Theory</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><br />
<p align="justify">In genetics, an operon is a functioning unit of genomic DNA containing a cluster of genes<br />
under the control of a single regulatory signal or promoter. The genes contained in the<br />
operon are either expressed together or not at all. Several genes must be both cotranscribed<br />
and co-regulated to define an operon.<br /><br /><br />
The first time "operon" was proposed is in a paper of French Academic Science, 1960.<br />
The lac operon of the model bacterium E. coli was discovered and provides a typical<br />
example of operon function. It consists a promoter, an operator, three structural genes and<br />
a terminator. The operon is regulated by several factors including the availability of glucose<br />
and lactose.<br /><br /><br />
From this paper, the so-called general theory of the operon was developed. According to<br />
the theory, all genes are controlled by means of operons through a single feedback<br />
regulatory mechanism-repression. The first operon to be described was the lac operon in<br />
E. coli. The 1965 Nobel Prize in Physiology and Medicine was awarded to François Jacob,<br />
André Michel Lwoff and Jacques Lucien Monod for their discoveries concerning the operon and virus synthesis.<br /><br />
</p><br />
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<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/7/7d/USTC_Software_Figure_1.png" /><br />
<p align="center"><strong>Figure 1.</strong> Structure of Operon</p></div><br />
<p align="justify">An operon is made up of several structural genes arranged under a common promoter and<br />
regulated by a common operator. It is defined as a set of adjacent structural genes, plus<br />
the adjacent regulatory signals that affect transcription of the structural genes. The<br />
regulators of a given operon, including repressors, corepressors and activators, are not<br />
necessarily coded for by that operon.<br /><br /><br />
As a unit of transcription, upstream of the structural genes lies a promoter sequence which<br />
provides a site for RNA polymerase to bind and initiate transcription. Close to the promoter<br />
lies a section of DNA called an operator.<br /><br /><br />
Operon regulation can be either negative or positive by induction or repression. Negative<br />
control involves the binding of a repressor to the operator to prevent transcription.<br />
Operons can also be positively controlled. An activator protein binds to DNA, usually at a<br />
site other than the operator, to stimulate transcription.<br />
</p><br />
<div align="center"><img style="width:600px;" src="https://static.igem.org/mediawiki/igem.org/2/25/USTC_Software_Figure_2.png"/><br />
<p align="justify"><strong>Figure 2.</strong> Regulation of Operon<br />
1: RNA Polymerase, 2: Repressor, 3: Promoter, 4: Operator, 5: Lactose, 6: lacZ, 7:<br />
lacY, 8: lacA. Top: The gene is essentially turned off. There is no lactose to inhibit the<br />
repressor, so the repressor binds to the operator, which obstructs the RNA polymerase<br />
from binding to the promoter and making lactase.Bottom: The gene is turned on.Lactose<br />
is inhibiting the repressor, allowing the RNA polymerase to bind with the promoter, and<br />
express the genes, which synthesize lactase. Eventually, the lactase will digest all of the<br />
lactose, until there is none to bind to the repressor. The repressor will then bind to the<br />
operator, stopping the manufacture of lactase.</p></div><br />
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<br />
</div><br />
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<div class="jobs_trigger"><strong>Regulatory Model</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">Regulation of gene expression includes four levels. We choose the transcriptional level to simulate the regulation both for its significance and model simplification.</p><br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/igem.org/8/87/USTC_Software_Figure_3.png" /><br />
<p><strong>Figure 3.</strong>Regulation of gene expression.<br />Our regulation model is built based on the operon theory.<br /> The promoter region is regarded as the main regulatory region.</p></div><br />
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<br />
<div class="jobs_trigger"> <strong>Similarity and Homology</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment. It is defined as the proportion of the common subsequence in the aligned sequence. Any two sequences share a certain<br />
similarity. It should be noted that similarity and homology are two different concepts.<br /><br /><br />
As with anatomical structures, homology between protein or DNA sequences is defined in<br />
terms of shared ancestry. Two segments of DNA can have shared ancestry because of<br />
either a speciation event or a duplication event. The terms “percent homology” and<br />
“sequence similarity” are often used interchangeably. As with anatomical structures, high<br />
sequence similarity might occur because of convergent evolution, or, as with shorter<br />
sequences, because of chance. Such sequences are similar but not homologous.<br />
Sequence regions that homologous are also called conserved.<br /><br /><br />
In our project, we use similarity to connect the exogenous gene with the original network.<br />
Because there is a good chance that the exogenous gene is not homologous with the<br />
genes in the network.</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The GRN matrix is the mathematical description of gene regulatory network in which “1” represents “enhance”, “-1” represents “repress” and “0” represents “no regulatory relationship”. The units(RU) in x-axis regulate the units in y-axis. A row can be seen as a vector containing all the information of the target(corresponding unit in the y-axis). Similarly, a column can be seen as a vector containing all the information of the regulator(corresponding unit in the x-axis).</p> <br />
</div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The sequence similarity is obtained by sequence alignment based on Needleman-Wunsch algorithm[FIXME: wiki link here]. The Needleman-Wunsch algorithm performs a global alignment on two protein sequences or nucleotide sequences. It was the first application of dynamic programming to biological sequence comparison.<br />
<br />
When dynamic programming is applicable, the method takes far less time than naive methods. Using a naive method, many of the subproblems are generated and sovled many times. The dynamic programming approach seeks to solve each subproblem only once. Once the solution to a given subproblem has been computed, it is stored to be looked up next time.<br />
<br />
[Pic. 5 Dynamic programming and naive method]<br />
<br />
Like the Needleman-Wunsch algorithm, of which it is a variation, Smith-Waterman is also a dynamic programming algorithm. But it is a local sequence alignment algorithm. The famous BLAST(Basic Local Alignment Search Tool) is improved from Smith-Waterman algorithm. Although local algorithm has the desirable property that it is guaranteed to find the optimal local alignment, we decided to choose the global one because we regarded the segment sequence as a unit.<br />
<br />
Sequences are aligned with different detailed methods in different situations. In the regulated side, what we care about is the DNA sequence. In the regulating side, it is the amino acid sequence. When it comes to predict the regulated behavior, we use a DNA substitution matrix to align promoter and protein coding sequences. In the prediction of regulating behavior, the substitution matrix BLOSUM_50 is used to align the amino acid sequences translated from protein coding sequences.<br />
<br />
The promoter similarities of the query unit and subject units are stored in a vector. The protein coding similarities are stored in another vector. These vectors are prepared to be used in the new network construction.<br />
</p> <br />
</div> <br />
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<br />
<div class="jobs_trigger"><strong>Needleman-Wunsch Algorithm</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The Needleman-Wunsch algorithm was first published in1970 by Saul B. Needleman and Christian D. Wunsch. It performs a global alignment of two sequences and is mostly used in bioinformatics to align protein or nucleotide sequence. Our software applied this algorithm in the alignment of DNA and amino acid sequences.<br/><br/><br />
<br />
The Needleman-Wunsch algorithm is one kind of dynamic programming and It was the first attempt in biological sequence comparison of dynamic programming.<br/><br/><br />
<br />
Here is an example of Needleman-Wunsch algorithm. S(a,b) is the similarity of character a and character b. The scores of characters are shown in the similarity matrix. We assume this matrix was<br />
</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/5/52/USTC_Software_DNA_S_M.png"/></div> <br />
<p>And we uses linear gap penalty, denoted by d, here, we set the gap penalty as -5.Then the alignment:</p><br />
<p align="center"><strong><em><br />
A: AGACTAGTTAC<br/><br />
B: CGA - - - GACGT<br />
</em></strong></p><br />
<br />
<p>would have the following score:</p><br />
<p align="center"><strong><em><br />
S(A,C)+S(G,C)+S(A,A)+(3)+S(G,G)+S(T,A)+S(T,C)+S(A,G)+S(C,T) = -3+7+10-(3x5)+7+(-4)+0+(-1)+0 = 1<br />
</em></strong></p><br />
<br />
<p align="justify">To find the highest score of alignment, in this algorithm, a two dimensional matrix F with sequences and scores was allocated. The score in row i, column j is denoted by Fij. There is one column for each character in sequence A and one row for each character in sequence B. Therefore, if we align sequences with sizes of n and m, the amount of memory taken up here is O(n,m).<br/><br/><br />
<br />
As the algorithm going on, Fij was calculated to be the optimal score by the principle as following:<br/><br />
Basis:<br />
</p><br />
<p align="center"><strong><em>Fi0 = d*i<br/>F0j = d*j</em></strong></p><br />
<p>Recursion:</p><br />
<p align="center"><strong><em>Fij = max(F(i-1,j-1) + S(Ai,Bj), F(i-1,j) + d, F(i,j-1) + d)</em></strong></p><br/><br />
<p>The pseudo-code of this algorithm would look like this:</p><br />
<br/><br />
<div id="pseudo"><p><br />
<strong> for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; F(i,0) <-- d*i<br/><br />
<strong> for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; F(0,j) <-- d*j<br/><br />
<strong>for</strong> i = 0 <strong>to length(A)</strong><br/><br />
&nbsp; <strong>for</strong> j = 0 <strong>to length(B)</strong><br/><br />
&nbsp; {<br/><br />
&nbsp; &nbsp; Match <-- F(i-1,j-1) + S(Ai,Bj)<br/><br />
&nbsp; &nbsp; Delete <-- F(i-1,j) + d<br/><br />
&nbsp; &nbsp; Insert <-- F(i,j-1) + d<br/><br />
&nbsp; &nbsp; F(i,j) <-- <strong>max</strong>(Match, Insert, Delete)<br/><br />
&nbsp; }<br />
</p><br />
</div><br />
<br />
<p align="justify">After the matrix F was computed, Fnm would be the maximum score among all possible alignment.<br/><br/><br />
<br />
If you want to see the optimal alignment, you can trace back from Fnm by comparing three possible sources mentioned in the above code (Match, Insert and Delete). If Match, then Aj and Bi are aligned, if Insert, Bi was aligned with a gap and if Delete, then Aj and a gap are aligned. Also, you may find there are not only one optimal alignment.<br/><br/><br />
As for the example, we would get the following matrix by applying Needleman Wunsch algorithm:</p><br />
<br />
<br />
<div align="center"><img src="https://static.igem.org/mediawiki/2013/e/e2/USTC_Software_arrow_game.png"/></div><br />
<p>And the optimal alignment would be:</p><br />
<br />
<p align="center"><strong><em>- - AGACTAGTTAC <br/><br />
CGAGAC - - GT - - -<br />
</em></strong></p><br />
<br />
<br />
</div><br />
<br />
<br />
<div class="jobs_trigger"><strong>A Supplementary Game</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">The rows and columns in the GRN matrix can be regarded as vectors containing the regulated or the regulating information. The behavior similarity of two units can be described by the dot product of two regulated vectors or two regulating vectors. Biologists usually think the more similar two sequences are, the more likely they have similar behaviors. Whether the ratio of genes with similar behaviors is positively correlated with gene similarity is essential to our project. So we obtained 1.6 million sets of data by pairwise alignment of all the 1748 units in the GRN of K-12. Each set of data consists of gene similarity and behavior similarity. The result is analyzed and plotted in the figure. The linear fit shows that the ratio is positively correlated with the similarity.</p><br/><br />
<br />
<div align="center"><img style="width:600px; height:auto;"src="https://static.igem.org/mediawiki/2013/d/d0/USTC_Software_Simi-Ratio.png<br />
" /><br />
<p><strong>Figure 4.</strong>Linear fit of ratio-similarity relationship.</p></div><br />
<p align="justify">Although there are examples that a slight change in DNA sequence will significantly change the property of the gene, for example, sickle-cell disease, the influence is usually determined by the location and scale of the mutation. So the result is still convincing to some degree.</p><br />
</div><br />
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<br />
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<div id="New_Network_Construction"><br />
<br />
<h2>New Network Construction</h2><br />
<br />
<br />
<div id="jobs_container"><br />
<div class="jobs_trigger"><strong>Random Noise</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">Normally, the similarity of two sequences will not be zero. Some computational<br />
experiments were carried out to study the random sequence similarities. We randomly<br />
chose a gene in the network and generated 1000 random sequences. The alignment result<br />
indicates that the random sequence similarities are Gauss distributed. The result suggests<br />
that some similarities are out of statistic significance.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/8/89/USTC_Software_Figure_4.png" /><br />
<p><strong>Figure 5.</strong> Random similarity distribution</p></div><br />
<br />
<br />
</div><br />
<br />
<div class="jobs_trigger"><strong>Filter</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">We need the genes highly similar to the exogenous one to interact with it. The program will<br />
align the exogenous gene(query) with genes in the network(subject) and get the original<br />
similarities. In order to filter meaningless low values, a certain amount of random<br />
sequences are generated for each query-subject alignment. Normally, 100 is sufficient.<br />
Because the sequence length will influence alignment result, random sequences are fixed<br />
at the same length as the query one. Then align random sequences with the subject<br />
sequence. The statistic result of these random similarities is used as a threshold.<br /><br />
<div align="center">Threshold = μ + xσ</div><br /><br />
In the formula, μ is the average random similarity. σ is the standard deviation. x is used to<br />
control the filter determined by machine learning. If the original similarity is lower than the<br />
threshold, it is abandoned. It is usually means the original value is usually short of<br />
statistical significance.<br /><br /><br />
An example about filtring and consistency is presented in “Example”.<br />
</p> <br />
</div> <br />
<br />
<br />
<br />
<div class="jobs_trigger"> <strong>Construct new GRN</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify">If there is a three-unit network and they interact with each other as it is shown in the figure.<br />
The regulation is described by the GRN matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/8/8a/USTC_Software_Figure_5.png" /><br />
<p align="justify"><strong>Figure 6.</strong> Example network and its GRN matrix.</p></div><br />
<br />
<br />
<p align="justify">If D is the exogenous unit, we can obtain three similarity data sets of D with the units in the<br />
original GRN: <br />
<li style="margin-left:40px;">Promoter sequence similarity</li><br />
<li style="margin-left:40px;">Gene sequence similarity</li><br />
<li style="margin-left:40px;">Amino acid sequence similarity.</li><br />
<p><br />
The construction is equivalent to add a new column and a row into the original matrix.</p><br />
<div align="center"><img src="https://static.igem.org/mediawiki/igem.org/9/97/USTC_Software_Figure_6.png" /><br />
<p><strong>Figure 7.</strong> Mathematical Equivalence</p></div><br />
<p align="justify">When filling the column, D is compared with the regulators of the unit in each row. The<br />
regulations in the row are consider separately and marked as “positive group” and<br />
“negative group”. The average similarity of each group represents the distance between<br />
the exogenous unit and the group. D is supposed to have the larger one's regulatory<br />
direction(positive or negative). The regulatory intensity is the weight average regulation of<br />
the chose group. The weight here is the amino acid sequence similarity.<br /><br /><br />
There are two conditions when fill the new row:<br /><br />
1. There are units having the same promoter as the exogenous unit.<br /><br />
2. There is no units having the same promoter as the exogenous unit.<br /><br /><br />
In condition 1, the units sharing the same promoter with the new member are picked out,<br />
and the following steps are the same as the construction of the column. The difference is<br />
the similarity used here is the gene sequence similarity. As explained in the regulation<br />
model part, the promoter is the main regulatory region, but the following sequence is also<br />
considered. Now the promoter is the same, so what we focus on are the gene sequences.<br /><br /><br />
In condition 2, the process is almost the same as constructing the new column. Promoter<br />
similarity is used because it is the main region.</p><br />
<div align="center"><br />
<img src="https://static.igem.org/mediawiki/igem.org/c/c5/USTC_Software_Figure_7.png" /><br />
<p><strong>Figure 8.</strong> Construct New GRN</p></div><br />
<br />
</div> <br />
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</div><br />
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<br />
<div id="Network_Model"><br />
<h2>Network Model</h2><br />
<br />
<div id="jobs_container"><br />
<br />
<br />
<br />
<div class="jobs_trigger"><strong>Network Model Abstract</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify">Network analysis includes finding stable condition of network, adding new gene, finding new stable condition and changes from original condition to new condition. We use densities of materials to describe network condition. If all material densities are time-invariant, we can say the network condition is stable.</p><br />
</div><br />
<br />
<br />
<div class="jobs_trigger"><strong>Hill Equations</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">Regulation relationship in genetic network includes positive regulation, negative regulation, positive-or-negative regulation and no regulation. We store regulation relationship in matrix R. Rji means the unit in line j and row i. For the material of original network, Rji=1 means material i enhance material j, Rji=-1 means material i repress material j, Rji=0 means material i has no influence on material j, Rji=2 means material i enhance or repress material j. For the new material, Rji ranges from -1 to 1. Rji<0 means the possibility of positive regulation is Rji; Rji>0 means the possibility of negative regulation is –Rji; Rji=0 means there is no regulation from i to j.<br />
We use Hill equations to describe intensity of regulation. Equations are like following:<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e0/USTC_Software_1.png" style="width:600px;"/><br />
<br/></br><br />
The left side of the equation is the derivative x(density) on t(time).”qi”,”pi”,”ri”,”mi”,”ni” are parameters, which determine the intensity of regulation."ri" is degradation rate. Mji is exponent. M is a matrix whose dimensions are equivalent to R's. Mji is 0 or ranges from 0.5 to 1.2 or ranges from -1.2 to 0.5. For the material of original network, if Rji=1,Mji ranges from 0.5 to 1.2;if Rji=-1, Mji ranges from -1.2 to -0.5; if Rji=2;Mji ranges from -1.2 to 0.5 or 0.5 to 1. These Mjis' absolute values are given randomly by program. If Rji=0, Mji=0. <br />
</br>For the new material,<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/6/64/USTC_Software_2.png"/><br />
<br/></br><br />
<br />
</p><br />
</div><br />
<br />
<div class="jobs_trigger"><strong>Find Stable Network Condition</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify"><br />
Stable condition is the condition in which densities are time-invariant. We store material densities in a vector and solve the differential equations with Euler's formula, which is like below<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/e/e6/USTC_Software_3.png" style="width:600px;"/><br />
<br/></br><br />
We know the network will be stable at last, so every material density has a limitation.<br />
<br />
</p><br />
</div><br />
<br />
<br />
<br />
<div class="jobs_trigger"> <strong>Find Changes From Original Stable Condition to New Condition</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">Record the original stable condition, set new material density to 0 and this is the new initial density vector. Solve new equations and record density vectors before the new condition is stable and store these data in a text file.</br></br><br />
<br />
To evaluate the new network, we introduce the grading system.<br />
<br/></br><br />
<img src="https://static.igem.org/mediawiki/2013/3/32/USTC_Software_4.png" style="width:600px;"/><br />
<img src="https://static.igem.org/mediawiki/2013/b/bc/USTC_Software_5.png" style="width:500px;"/><br />
<br />
<br/></br><br />
"xi" and "Xi" are densities of material i, which is not the new material."ny" is the number of materials. The more new densities are close to the original, the less the influence the cell endues. In general, cells close to the original cell are more likely to survive than those who are far different from the original cell. That is the thought of the grading system.</br></br><br />
We did a lot of running and found that the “AbsValue” ranges from 0 to 370, so "ScoreA" ranges from 0 to 4.9.We get the integer part and store it in an array, which has five sections. Generate 100 or 200 matrix M from matrix R and run the original and new network for each M, so we can get 100 or 200 of "ScoreA"s. The section which has maximum "ScoreA"s is the eventual score.<br />
</p> <br />
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<div id="Predict"><br />
<h2>Predict</h2><br />
<br />
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<br />
<div id="jobs_container"><br />
<br />
<br />
<br />
<div class="jobs_trigger"><strong>Predict Abstract</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify">In some cases, importing exogenous gene is for enhancing or suppressing the expression of some specific genes in engineered bacteria itself. But it is hard to choose an appropriate regulatory gene. Our software analyzes the GRN forward as well as simulates by optimization algorithm backward for giving a reference of choosing to the users. Our software not only focused on the direct regulation but also focused on the global GRN. In the same time, controlling the expression of multiple genes in network has been realized by global prediction. What's more, Particle Swarm Optimization (PSO) Algorithm makes it possible.</p><br />
</div><br />
<br />
<br />
<div class="jobs_trigger"><strong>Input Target</strong></div><br />
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">Before prediction, the expression of specific genes which the experimenter needs should be input into our software as well as the improvement or depression. The number of target gene is SEVEN at most.</br></br><br />
It is a must that figuring out the strongest and weakest expression strength before inputting the extreme cases into the target expression. The way to find out the strongest and weakest expression is modeling the GRN's steady state by a large amount of random regulation from -1 and 1. On the other hand, the expression of genes unpicked by the users should be stable as much as possible. The initial strength of expression is calculated by modeling the original GRN with Hill's equation.<br />
</p><br />
</div><br />
<br />
<div class="jobs_trigger"><strong>Particle Swarm Optimization</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify"><br />
For getting the best regulation, our software uses PSO algorithm based on 30 particles to simulate the GRN's changing. First of all, the interactions of regulator and regulated-by have been put into those particles in random so that each particle will have the whole set of regulation. Secondly, the variance between target expressions and stable expression of new GRN have been regarded as the optimize requirements in PSO algorithm. As a result, the minimal variance of 30 particles is the global optimum and the minimal variance of the procession in one particle is the local optimum. Then, taking a step towards global and local optimum as well as considering the inertia and perturbation avoids falling into the sub-optimal condition.</br></br><br />
At last, when the variance of expression reaches an acceptable range, our software picks out and saves the best global optimum particle following by the movement of those particles stop.</br></br><br />
We constantly revises the factors in PSO algorithm by machine learning method for accurate simulation with a fast PSO particle-motion equation. At the same time, our software also filter the result of regulatory value which is more intuitive.<br />
</p><br />
</div><br />
<br />
<br />
<br />
<div class="jobs_trigger"> <strong>Filter</strong></div><br />
<div class="jobs_item" style="display: none;"><p align="justify">To improve the efficiency of choosing a suitable gene after getting a series of regulatory value, our software picks out some obvious regulation. The value of regulation is between -1 to 1 in which -1 means negative effect and 1 means positive effect. As a result, what our software has done is filtering out the absolute value which is lower than 0.9. Because the difference of regulatory intensity lower than 0.1 has very little effect to the stable expression, the final result of regulation is indicated by Boolean value.</br></br><br />
The format of regulatory prediction in “Result”:</br><br />
Gene_name->Gene_name regulation(+/-)<br />
<br />
</p> <br />
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<div id="Database"><br />
<h2>Database</h2><br />
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<div id="jobs_container"><br />
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<br />
<div class="jobs_trigger"><strong>TF-TF</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify">This file contains the regulation between Transcription Factors.</p><br />
<img src="https://static.igem.org/mediawiki/2013/6/69/USTC_Software_TT.jpg"/><br />
</div><br />
<br />
<br />
<div class="jobs_trigger"><strong>TF-Gene</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify">This file contains the regulation between Transcription Factors and Genes</p><br />
<img src="https://static.igem.org/mediawiki/2013/4/47/USTC_Software_TG.jpg"/><br />
</div><br />
<br />
<div class="jobs_trigger"><strong>Gene Info</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify">This file contains the information about all genes in E-coli K-12</p><br />
<img src="https://static.igem.org/mediawiki/2013/4/45/USTC_Software_GI.jpg"/><br />
</div><br />
<br />
<br />
<div class="jobs_trigger"><strong>Promoter Info</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify">This file contains the information about all promoters in E-coli K-12</p><br />
<img src="https://static.igem.org/mediawiki/2013/8/8a/USTC_Software_PI.jpg"/><br />
</div><br />
<br />
<div class="jobs_trigger"><strong>TU Info</strong></div><br />
<div class="jobs_item" style="display: block;"><p align="justify">This file contains the information about all Transcription Units in E-coli K-12</p><br />
<img src="https://static.igem.org/mediawiki/2013/1/1e/USTC_Software_TI.jpg"/><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/OverallTeam:USTC-Software/Project/Overall2013-10-27T03:28:32Z<p>USTCkun: </p>
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<h1 align="justify">Overall</h1> <br />
<h2 align="justify">Introduction</h2><br />
<p align="justify">Synthetic Biology is the design and construction of biological devices and systems for useful purposes. It is an area of biological research and technology that combines biology and engineering. <a class="content" href="http://en.wikipedia.org/wiki/Synthetic_Biology">(From Wikipedia)</a> As a result, it is inevitable to import exogenous gene into engineered bacteria to enhance special protein’s expression. But the result of import couldn’t be found out without cumbersome wet lab experiments. There will be some results out of control because the infect of gene regulatory network(GRN) is rarely considered when finding imported gene.<br/><br/><br />
Gene Network Analyze and Predict (gNAP) is a software which can model the interaction between genes and figure out the expression of all genes in new GRN. Using alignment method, gNAP predicts the interactions between new gene and original genes to construct a new GRN. What’s more, gNAP’s inverse modeling based on overall GRN gives advice to experimenters before choosing the imported gene meeting their purpose.<br/><br/><br />
gNAP is the FIRST software analyzing the infection of importing new exogenous gene into engineered bacteria. Take a gNAP before your experiment!<br />
</p><br />
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<div id="Software"><br />
<h2 align="justify">Software</h2><br />
<p align="justify">gNAP’s source code is written in C++ programming language and its visualization parts are written in Java programming language. The GUI is designed with <a class="content" href="http://qt-project.org/">Qt</a> which is based on C++ also. As a result, all of them can be compiled across various platforms. The executable program has been built on Windows, Linux and MacOS operating systems.<br/><br/><br />
There are four major parts in gNAP called Start, Monitor, Result and Display. “Start” is used to fetch the information from data downloaded from Online Database by users. When software is running, the part of “Monitor” is like a monitor which could observe the process of analyze and predict. All succinct results can be seen in “Result” part letting users have a broad view. “Display” written in JAVA is the last part displaying the results in an intuitive visual graphical interface.<br/><br/><br />
gFinder(gene finder)'s source code is written in C++ language and it is a website software which is really convinient. You can use this software just click <a class="content" href="http://www.stlover.org/gFinder">here</a>. All source code of this software is also push up to our <a class="content" href="https://github.com/igemsoftware/USTC-Software2013/">github</a>.</br></br><br />
gRNA(gene regulatory network analists) is a gaming program for children who interested in network analysis. Through this game they can cultivate the interest of biology and have a visual image of complex regulatory network. The calculation module is as same as the modeling of GRN in our major software “gNAP”. To improve the playability of this app, we reduced the difficulty of analysis in purpose to investigate the analytical thinking ability of complex network regulation.<br />
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<div id="Methodology"><br />
<h2 align="justify">Methodology</h2><br />
<p align="justify">In order to simulate the GRN’s working and analyze the change after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm. To meet need of the analysis and prediction, our team innovative improved most of algorithms and methods to make the simulation more accurate and more efficient.<br/><br/><br />
gNAP uses Binary Tree to filtrate useful information from database and construct them together as resource for simulation afterward. Needle-Wunsch Alignment Algorithm, Decision Tree method and Hill Equation are utilized to analyze the new GRN. On the other side, to figure out an advise for choosing imported gene, PSO Algorithm finds out the best interaction meeting users’ needs.</p><br />
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<h2 align="justify">Database</h2><br />
<p align="justify"> The database used in gNAP is flexible because the software could construct GRN by any data sources. But because our team use <a class="content" href="http://regulondb.ccg.unam.mx/index.jsp">RegulonDB</a> as an example to test gNAP, fetching mode built-in gNAP now only suit to the form of RegulonDB’s files. It is very easy to change the format of fetching based on the database. It should be a must that the files downloaded contain all information needed in the software. Such as gene sequence, promoter sequence, mutual regulation, gene name and so on.<br/><br/><br />
The built-in database in gNAP is Escherichia coli K-12 database which contains interaction and information about all genes. The software choose TF(Transcription Factor) to TF Database, TF to Gene Database, Gene Info Database, Promoter Info Database, TU(Transcription Unit) Info Database.<a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/index.jsp">[download here]</a><br />
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<h1 align="justify">Overall</h1> <br />
<h2 align="justify">Introduction</h2><br />
<p align="justify">Synthetic Biology is the design and construction of biological devices and systems for useful purposes. It is an area of biological research and technology that combines biology and engineering. <a class="content" href="http://en.wikipedia.org/wiki/Synthetic_Biology">(From Wikipedia)</a> As a result, it is inevitable to import exogenous gene into engineered bacteria to enhance special protein’s expression. But the result of import couldn’t be found out without cumbersome wet lab experiments. There will be some results out of control because the infect of genetic regulatory network(GRN) is rarely considered when finding imported gene.<br/><br/><br />
Genetic Network Analyze and Predict (gNAP) is a software which can model the interaction between genes and figure out the expression of all genes in new GRN. Using alignment method, gNAP predicts the interactions between new gene and original genes to construct a new GRN. What’s more, gNAP’s inverse modeling based on overall GRN gives advice to experimenters before choosing the imported gene meeting their purpose.<br/><br/><br />
gNAP is the FIRST software analyzing the infection of importing new exogenous gene into engineered bacteria. Take a gNAP before your experiment!<br />
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<p align="justify">gNAP’s source code is written in C++ programming language and its visualization parts are written in Java programming language. The GUI is designed with <a class="content" href="http://qt-project.org/">Qt</a> which is based on C++ also. As a result, all of them can be compiled across various platforms. The executable program has been built on Windows, Linux and MacOS operating systems.<br/><br/><br />
There are four major parts in gNAP called Start, Monitor, Result and Display. “Start” is used to fetch the information from data downloaded from Online Database by users. When software is running, the part of “Monitor” is like a monitor which could observe the process of analyze and predict. All succinct results can be seen in “Result” part letting users have a broad view. “Display” written in JAVA is the last part displaying the results in an intuitive visual graphical interface.<br/><br/><br />
gFinder(gene finder)'s source code is written in C++ language and it is a website software which is really convinient. You can use this software just click <a class="content" href="http://www.stlover.org/gFinder">here</a>. All source code of this software is also push up to our <a class="content" href="https://github.com/igemsoftware/USTC-Software2013/">github</a>.</br></br><br />
gRNA(gene regulatory network analists) is a gaming program for children who interested in network analysis. Through this game they can cultivate the interest of biology and have a visual image of complex regulatory network. The calculation module is as same as the modeling of GRN in our major software “gNAP”. To improve the playability of this app, we reduced the difficulty of analysis in purpose to investigate the analytical thinking ability of complex network regulation.<br />
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<h2 align="justify">Methodology</h2><br />
<p align="justify">In order to simulate the GRN’s working and analyze the change after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm. To meet need of the analysis and prediction, our team innovative improved most of algorithms and methods to make the simulation more accurate and more efficient.<br/><br/><br />
gNAP uses Binary Tree to filtrate useful information from database and construct them together as resource for simulation afterward. Needle-Wunsch Alignment Algorithm, Decision Tree method and Hill Equation are utilized to analyze the new GRN. On the other side, to figure out an advise for choosing imported gene, PSO Algorithm finds out the best interaction meeting users’ needs.</p><br />
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<br />
<br />
<div id="Methodology"><br />
<h2 align="justify">Database</h2><br />
<p align="justify"> The database used in gNAP is flexible because the software could construct GRN by any data sources. But because our team use <a class="content" href="http://regulondb.ccg.unam.mx/index.jsp">RegulonDB</a> as an example to test gNAP, fetching mode built-in gNAP now only suit to the form of RegulonDB’s files. It is very easy to change the format of fetching based on the database. It should be a must that the files downloaded contain all information needed in the software. Such as gene sequence, promoter sequence, mutual regulation, gene name and so on.<br/><br/><br />
The built-in database in gNAP is Escherichia coli K-12 database which contains interaction and information about all genes. The software choose TF(Transcription Factor) to TF Database, TF to Gene Database, Gene Info Database, Promoter Info Database, TU(Transcription Unit) Info Database.<a class="content" href="http://regulondb.ccg.unam.mx/menu/download/datasets/index.jsp">[download here]</a><br />
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<div class="camera_caption fadeFromBottom"><em>Take a gNAP before wearing your gloves!<br/>Genetic Network Analyze and Predict</em></div><br />
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<div data-thumb="https://static.igem.org/mediawiki/igem.org/9/99/USTC_Software_temp2.jpg<br />
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<div class="camera_caption fadeFromBottom"><em>The sketch and final GUI of gNAP!</em></div><br />
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<div data-thumb="https://static.igem.org/mediawiki/igem.org/5/5b/USTC_Software_temp3.jpg" data-src="https://static.igem.org/mediawiki/igem.org/0/0d/USTC_Software_three.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We compare the result of our software with gene expression profile in literature.</em></div><br />
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<div data-thumb="https://static.igem.org/mediawiki/2013/8/80/USTC_Software_4s.jpg" data-src="https://static.igem.org/mediawiki/2013/c/cb/USTC_Software_4.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We are USTC-Software!</em></div><br />
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<h2>Overall</h2><br />
<p align="justify">Gene Network Analyze and Predict (gNAP) is a software which can model and analyze the change of new GRN constructing after exogenous gene’s import. What’s more, gNAP's reverse analysis based on global GRN gives some advise to experimenters before choosing the imported gene meeting their purpose.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Overall" class="more"></a><br />
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<h2><span class="head">Software</span></h2><br />
<p align="justify"><span style="font-family:Arial, Helvetica, sans-serif;">gNAP, gene network analyze and predict, is our main software which could analyze the input gene’s effects forward and reversely. gFinder, gene finder, is a website software which could find a specific gene in a database by gene’s function of regulation. gRNA, gene regulatory network analysts, is a small game which could improve the children’s ability of network analysis.<br/></span></p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Software" class="more"></a><br />
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<h2>Methodologies</h2><br />
<p align="justify">In order to simulate the GRN’s working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</p><br />
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<a href="https://2013.igem.org/Team:USTC-Software/Project/Method" class="more"></a><br />
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<h2><span class="head">Communication</span></h2><br />
<p align="justify">Our team hope to create a better community for iGEM software, so we <a href="https://2013.igem.org/Team:USTC-Software/collaboration" id="homelink">collaborate</a> with other teams. Open source on github with detailed <a href="https://2013.igem.org/Team:USTC-Software/Project/Develop" id="homelink">API</a> of our source code make our software’s development more easy and convenient. What’s more, to <a href="https://2013.igem.org/Team:USTC-Software/Human_practice" id="homelink">popularize</a> iGEM in our campus, we also give “Hi” from iGEM to all the students and teachers and give their “Hello” back.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Human_practice" class="more"></a><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-SoftwareTeam:USTC-Software2013-10-27T03:12:18Z<p>USTCkun: </p>
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<div class="camera_caption fadeFromBottom"><em>Take a gNAP before wearing your gloves!<br/>Genetic Network Analyze and Predict</em></div><br />
</div><br />
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<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/9/99/USTC_Software_temp2.jpg<br />
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<div class="camera_caption fadeFromBottom"><em>The sketch and final GUI of gNAP!</em></div><br />
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<div data-thumb="https://static.igem.org/mediawiki/igem.org/5/5b/USTC_Software_temp3.jpg" data-src="https://static.igem.org/mediawiki/igem.org/0/0d/USTC_Software_three.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We compare the result of our software with gene expression profile in literature.</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/2013/8/80/USTC_Software_4s.jpg" data-src="https://static.igem.org/mediawiki/2013/c/cb/USTC_Software_4.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We are USTC-Software!</em></div><br />
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</div><br />
<br />
<br />
<div id="green"><br />
<div class="right"><br />
<h2>Overall</h2><br />
<p align="justify">Gene Network Analyze and Predict (gNAP) is a software which can model and analyze the change of new GRN constructing after exogenous gene’s import. What’s more, gNAP's reverse analysis based on global GRN gives some advise to experimenters before choosing the imported gene meeting their purpose.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Overall" class="more"></a><br />
</div><br />
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<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/a/a6/USTC_Software_Overall.png"/><br />
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<div class="right"><br />
<h2><span class="head">Software</span></h2><br />
<p align="justify"><span style="font-family:Arial, Helvetica, sans-serif;">gNAP, gene network analyze and predict, is our main software which could analyze the input gene’s effects forward and reversely. gFinder, gene finder, is a website software which could find a specific gene in a database by gene’s function of regulation. gRNA, gene regulatory network analysts, is a small game which could improve the children’s ability of network analysis.<br/></span></p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Software" class="more"></a><br />
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<div class="right"><br />
<h2>Methodologies</h2><br />
<p align="justify">In order to simulate the GRN’s working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</p><br />
<br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Method" class="more"></a><br />
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<h2><span class="head">Communication</span></h2><br />
<p align="justify">Our team hope to create a better community for iGEM software, so we <a href="https://2013.igem.org/Team:USTC-Software/collaboration" id="homelink">collaborate</a> with other teams. Open source on github with detailed <a href="https://2013.igem.org/Team:USTC-Software/Project/Develop" id="homelink">API</a> of our source code make our software’s development more easy and convenient. What’s more, to popularize iGEM in our campus, we also give “Hi” from iGEM to all the students and teachers and give their “Hello” back.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Human_practice" class="more"></a><br />
</div><br />
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<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c0/USTC_Software_Humanpractice.png"/><br />
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<h2>Sponsors</h2><br />
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<a href="http://china.db.com/index_e.html"><img src='https://static.igem.org/mediawiki/2012/f/fc/USTC-Software-images-db-logo.png' /></a><br />
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<a href="http://en.physics.ustc.edu.cn//"><img src='https://static.igem.org/mediawiki/2012/f/f5/USTC-Software-images-physics-logo.png' /></a><br />
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<a href="http://en.biox.ustc.edu.cn/"><img src='https://static.igem.org/mediawiki/2012/2/27/USTC-Software-images-life-logo.png' /></a><br />
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" data-src="https://static.igem.org/mediawiki/2013/8/85/USTC_Software_Page1.png<br />
"><br />
<div class="camera_caption fadeFromBottom"><em>Take a gNAP before wearing your gloves!<br/>Genetic Network Analyze and Predict</em></div><br />
</div><br />
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<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/9/99/USTC_Software_temp2.jpg<br />
" data-src="https://static.igem.org/mediawiki/2013/1/15/2.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>The sketch and final GUI of gNAP!</em></div><br />
</div><br />
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<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/5/5b/USTC_Software_temp3.jpg" data-src="https://static.igem.org/mediawiki/igem.org/0/0d/USTC_Software_three.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We compare the result of our software with gene expression profile in literature.</em></div><br />
</div><br />
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<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/a/ae/USTC_Software_temp4.jpg" data-src="https://static.igem.org/mediawiki/2013/c/cb/USTC_Software_4.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We are USTC-Software!</em></div><br />
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<div id="green"><br />
<div class="right"><br />
<h2>Overall</h2><br />
<p align="justify">Genetic Network Analyze and Predict (gNAP) is a software which can model and analyze the change of new GRN constructing after exogenous gene’s import. What’s more, gNAP’s inverse prediction based on overall GRN gives some advise to experimenters before choosing the imported gene meeting their purpose.</p><br />
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<div id="cyan"><br />
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<div class="right"><br />
<h2><span class="head">Software</span></h2><br />
<p align="justify"><span style="font-family:Arial, Helvetica, sans-serif;">gNAP, genetic network analyze and predict, is our main software which could analyze the input gene’s effects forward and reversely. gFinder, gene finder, is a website software which could find a specific gene in a database by gene’s function of regulation. gRNA, genetic regulatory network analysts, is a small game which could improve the children’s ability of network analysis.<br/></span></p><br />
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<div id="blue"><br />
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<div class="right"><br />
<h2>Methodologies</h2><br />
<p align="justify">In order to simulate the GRN’s working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</p><br />
<br />
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<h2><span class="head">Communication</span></h2><br />
<p align="justify">Our team hope to create a better community for iGEM software, so we collaborate with other teams. Open source on github with detailed API of our source code make our software’s development more easy and convenient. What’s more, to popularize iGEM in our campus, we also give “Hi” from iGEM to all the students and teachers and give their “Hello” back.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Human_practice" class="more"></a><br />
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<h2>Sponsors</h2><br />
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<div class='mask1'></div><br />
<a href="http://china.db.com/index_e.html"><img src='https://static.igem.org/mediawiki/2012/f/fc/USTC-Software-images-db-logo.png' /></a><br />
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<a href="http://www.ustcif.org/default.php"><img src='https://static.igem.org/mediawiki/2012/d/d9/USTC-Software-images-ustcif-logo.png' /></a><br />
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<a href="http://en.physics.ustc.edu.cn//"><img src='https://static.igem.org/mediawiki/2012/f/f5/USTC-Software-images-physics-logo.png' /></a><br />
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<a href="http://en.biox.ustc.edu.cn/"><img src='https://static.igem.org/mediawiki/2012/2/27/USTC-Software-images-life-logo.png' /></a><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-SoftwareTeam:USTC-Software2013-10-24T13:40:01Z<p>USTCkun: </p>
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" data-src="https://static.igem.org/mediawiki/2013/8/85/USTC_Software_Page1.png<br />
"><br />
<div class="camera_caption fadeFromBottom"><em>Take a gNAP before wearing your gloves!<br/>Genetic Network Analyze and Predict</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/9/99/USTC_Software_temp2.jpg<br />
" data-src="https://static.igem.org/mediawiki/2013/1/15/2.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>The sketch and final GUI of gNAP!</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/5/5b/USTC_Software_temp3.jpg" data-src="https://static.igem.org/mediawiki/igem.org/0/0d/USTC_Software_three.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We compare the result of our software with gene expression profile in literature.</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/a/ae/USTC_Software_temp4.jpg" data-src="https://static.igem.org/mediawiki/2013/c/cb/USTC_Software_4.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We are USTC-Software!</em></div><br />
</div><br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
<div id="green"><br />
<div class="right"><br />
<h2>Overall</h2><br />
<p align="justify">Genetic Network Analyze and Predict (gNAP) is a software which can model and analyze the change of new GRN constructing after input gene’s import. What’s more, gNAP’s inverse prediction based on overall GRN gives some advise to experimenters before choosing the imported gene meeting their purpose.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Overall" class="more"></a><br />
</div><br />
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</div><br />
<br />
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</div><br />
<br />
<br />
<div id="cyan"><br />
<br />
<div class="right"><br />
<h2><span class="head">Software</span></h2><br />
<p align="justify"><span style="font-family:Arial, Helvetica, sans-serif;">gNAP, genetic network analyze and predict, is our main software which could analyze the exogenous gene’s effects forward and reversely. gFinder, gene finder, is a website software which could find a specific gene in a database by gene’s function of regulation. gRNA, genetic regulatory network analysts, is a small game which could improve the children’s ability of network analysis.<br/></span></p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Software" class="more"></a><br />
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</div><br />
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</div><br />
<br />
<br />
<br />
<div id="blue"><br />
<br />
<div class="right"><br />
<h2>Methodologies</h2><br />
<p align="justify">In order to simulate the GRN’s working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</p><br />
<br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Method" class="more"></a><br />
</div><br />
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<div class="left"><br />
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<br />
<div id="purple"><br />
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<div class="right"><br />
<h2><span class="head">Communication</span></h2><br />
<p align="justify">Our team hope to create a better community for iGEM software, so we collaborate with other teams. Open source on github with detailed API of our source code make our software’s development more easy and convenient. What’s more, to popularize iGEM in our campus, we also give “Hi” from iGEM to all the students and teachers and give their “Hello” back.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Human_practice" class="more"></a><br />
</div><br />
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<div class="left"><br />
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<h2>Sponsors</h2><br />
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<div class="img" id="img"><br />
<div class='mask1'></div><br />
<a href="http://china.db.com/index_e.html"><img src='https://static.igem.org/mediawiki/2012/f/fc/USTC-Software-images-db-logo.png' /></a><br />
</div><br />
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<div class='img' id='img'><br />
<div class='mask2'></div><br />
<a href="http://www.ustcif.org/default.php"><img src='https://static.igem.org/mediawiki/2012/d/d9/USTC-Software-images-ustcif-logo.png' /></a><br />
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<a href="http://en.physics.ustc.edu.cn//"><img src='https://static.igem.org/mediawiki/2012/f/f5/USTC-Software-images-physics-logo.png' /></a><br />
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<a href="http://en.biox.ustc.edu.cn/"><img src='https://static.igem.org/mediawiki/2012/2/27/USTC-Software-images-life-logo.png' /></a><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-SoftwareTeam:USTC-Software2013-10-24T13:39:21Z<p>USTCkun: </p>
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" data-src="https://static.igem.org/mediawiki/2013/8/85/USTC_Software_Page1.png<br />
"><br />
<div class="camera_caption fadeFromBottom"><em>Take a gNAP before wearing your gloves!<br/>Genetic Network Analyze and Predict</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/9/99/USTC_Software_temp2.jpg<br />
" data-src="https://static.igem.org/mediawiki/2013/1/15/2.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>The sketch and final GUI of gNAP!</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/5/5b/USTC_Software_temp3.jpg" data-src="https://static.igem.org/mediawiki/igem.org/0/0d/USTC_Software_three.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We compare the result of our software with gene expression profile in literature.</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/a/ae/USTC_Software_temp4.jpg" data-src="https://static.igem.org/mediawiki/2013/c/cb/USTC_Software_4.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We are USTC-Software!</em></div><br />
</div><br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
<div id="green"><br />
<div class="right"><br />
<h2>Overall</h2><br />
<p align="justify">Genetic Network Analyze and Predict (gNAP) is a software which can model and analyze the change of new GRN constructing after exogenous gene’s import. What’s more, gNAP’s inverse prediction based on overall GRN gives some advise to experimenters before choosing the imported gene meeting their purpose.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Overall" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/a/a6/USTC_Software_Overall.png"/><br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
<div id="cyan"><br />
<br />
<div class="right"><br />
<h2><span class="head">Software</span></h2><br />
<p align="justify"><span style="font-family:Arial, Helvetica, sans-serif;">gNAP, genetic network analyze and predict, is our main software which could analyze the exogenous gene’s effects forward and reversely. gFinder, gene finder, is a website software which could find a specific gene in a database by gene’s function of regulation. gRNA, genetic regulatory network analysts, is a small game which could improve the children’s ability of network analysis.<br/></span></p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Software" class="more"></a><br />
</div><br />
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<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6e/USTC_Software_Software.png" /><br />
</div><br />
<br />
</div><br />
<br />
<br />
<br />
<div id="blue"><br />
<br />
<div class="right"><br />
<h2>Methodologies</h2><br />
<p align="justify">In order to simulate the GRN’s working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</p><br />
<br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Method" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d7/USTC_Software_Method.png" /><br />
</div><br />
<br />
</div><br />
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<br />
<br />
<br />
<div id="purple"><br />
<br />
<div class="right"><br />
<h2><span class="head">Communication</span></h2><br />
<p align="justify">Our team hope to create a better community for iGEM software, so we collaborate with other teams. Open source on github with detailed API of our source code make our software’s development more easy and convenient. What’s more, to popularize iGEM in our campus, we also give “Hi” from iGEM to all the students and teachers and give their “Hello” back.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Human_practice" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c0/USTC_Software_Humanpractice.png"/><br />
</div><br />
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</div><br />
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<div id="sponsors"><br />
<div style="margin-left:100px;"><br />
<h2>Sponsors</h2><br />
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<div class="img" id="img"><br />
<div class='mask1'></div><br />
<a href="http://china.db.com/index_e.html"><img src='https://static.igem.org/mediawiki/2012/f/fc/USTC-Software-images-db-logo.png' /></a><br />
</div><br />
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<div class='img' id='img'><br />
<div class='mask2'></div><br />
<a href="http://www.ustcif.org/default.php"><img src='https://static.igem.org/mediawiki/2012/d/d9/USTC-Software-images-ustcif-logo.png' /></a><br />
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<a href="http://en.physics.ustc.edu.cn//"><img src='https://static.igem.org/mediawiki/2012/f/f5/USTC-Software-images-physics-logo.png' /></a><br />
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<a href="http://en.biox.ustc.edu.cn/"><img src='https://static.igem.org/mediawiki/2012/2/27/USTC-Software-images-life-logo.png' /></a><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-SoftwareTeam:USTC-Software2013-10-24T13:36:07Z<p>USTCkun: </p>
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<div class="camera_caption fadeFromBottom"><em>Take a gNAP before wearing your gloves!<br/>Genetic Network Analyze and Predict</em></div><br />
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<div class="camera_caption fadeFromBottom"><em>We compare the result of our software with gene expression profile in literature.</em></div><br />
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<h2>Overall</h2><br />
<p align="justify">Genetic Network Analyze and Predict (gNAP) is a software which can model and analyze the change of new GRN constructing after exogenous gene’s import. What’s more, gNAP’s inverse prediction based on overall GRN gives some advise to experimenters before choosing the imported gene meeting their purpose.</p><br />
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<p align="justify"><span style="font-family:Arial, Helvetica, sans-serif;">gNAP, genetic network analyze and predict, is our main software which could analyze the exogenous gene’s effects forward and reversely. gFinder, gene finder, is a website software which could find a specific gene in a database by gene’s function of regulation. It could cooperate with gNAP. gRNA, genetic regulatory network analysts, is a small game which could improve the children’s ability of network analysis.<br/></span></p><br />
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<h2>Methodologies</h2><br />
<p align="justify">In order to simulate the GRN’s working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</p><br />
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<h2><span class="head">Communication</span></h2><br />
<p align="justify">Our team hope to create a better community for iGEM software, so we collaborate with other teams. Open source on github with detailed API of our source code make our software’s development more easy and convenient. What’s more, to popularize iGEM in our campus, we also give “Hi” from iGEM to all the students and teachers and give their “Hello” back.</p><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC_CHINA/TeamTeam:USTC CHINA/Team2013-09-27T15:36:39Z<p>USTCkun: </p>
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<h2>Students</h2><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/2/21/Zhangsitao1.JPG" alt="Zhang Sitao"> <br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/0/0d/Zhangsitao2.JPG" alt="Zhang Sitao"><br />
<div class ="nameplate"><a href="#">Zhang Sitao</a> </div><br />
<div class = "details"><br />
<img src="https://static.igem.org/mediawiki/2013/0/0d/Zhangsitao2.JPG" alt="Zhang Sitao" align="left"><p>Our labor leader weighs various matters, leads the overall trend and plays our cards right. He leaves a strong impression in others’ mind. However, His friends found that the leader is very cute.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/0/02/Zhaochanglong1.JPG" alt="Changlong Zhao"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/7/7e/Zhaochanglong2.JPG" alt="Changlong Zhao" ><br />
<div class ="nameplate"><a href="#">Changlong Zhao</a></div><br />
<div class = "details"><br />
<img src="https://static.igem.org/mediawiki/2013/7/7e/Zhaochanglong2.JPG" alt="Changlong Zhao" align="left"><p>There’s no doubt that we can give full stars for Changlong’s fighting capacity. The roads leading to success will never be smooth and Changlong is a perfect companion to travel with.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/9/91/Xionghanjin1.JPG" alt="Hanjin Xiong"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/d/d9/Xionghanjin2.JPG" alt="Hanjin Xiong" ><br />
<div class ="nameplate"><a href="#">Hanjin Xiong</a></div><br />
<div class = "details"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d9/Xionghanjin2.JPG" alt="Hanjin Xiong" align="left"><p>As the keynote speaker of our team, he always keeps a clear head with extraordinary creativity and expressiveness. He said, “It is shameful if you haven’t burnt the midnight oil for iGEM.” Moving forward bravely, he shows us overwhelming power which nobody can stop it.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/2/23/Limingyue1.JPG" alt="Mingyue Li"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/0/05/Limingyue2.JPG" alt="Mingyue Li"><br />
<div class ="nameplate"><a href="#">Mingyue Li</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/0/05/Limingyue2.JPG" alt="Mingyue Li" align="left"><p>“Mingyue Bacteria” is the spokesperson of our bacterium, handling the destiny of those little lives. We all agreed that, Mingyue with rubber gloves is GORGEOUSNESS!</p></div><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/8/85/Shenshengqi1.JPG" alt="Shen Shengqi"><br />
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<div class ="nameplate"><a href="#">Shen Shengqi</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/4/44/Shenshengqi2.JPG" alt="Shen Shengqi" align="left"><p>Everyone considers that it is honored to be a friend of “Brother Face” as he is a totally “local tyrant”. Actually,” Brother Shen” is warmth, nice, really expert in digging shortcuts in the experiments. He is a sharp soldier of our team as he adheres to the “more with less” principle.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/c/c1/Madanyi1.JPG" alt="Danyi Ma"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/1/19/Madanyi2.JPG" alt="Danyi Ma" ><br />
<div class ="nameplate"><a href="#">Danyi Ma</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/1/19/Madanyi2.JPG" alt="Danyi Ma" align="left"><p>”Aunty Ma” takes charge of our finance and safety, which calls for much patience and responsibility. In the experiment, she also plays an absolutely necessary role.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/8/87/Zhangheng1.JPG" alt="Zhang Heng"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/6/63/Zhangheng2.JPG" alt="Zhang Heng" ><br />
<div class ="nameplate"><a href="#">Zhang Heng</a> </div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/6/63/Zhangheng2.JPG" alt="Zhang Heng" align="left"><p>He is a man full of responsibility, we could 100% trust him! Bro, it’s you that bring us positive energy!</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/f/f9/Yuanye1.JPG" alt="Yvette Yuan"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/8/81/Yuanye2.JPG" alt="Yvette Yuan" ><br />
<div class ="nameplate"><a href="#">Yvette Yuan</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/8/81/Yuanye2.JPG" alt="Yvette Yuan" align="left"><p>As a s pronoun for efficient, Yuan Ye is studious and decisive. And what makes her best is that she always brings us delicious oranges.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/a/a3/Xinghuayue1.JPG" alt="Huayue Xing"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/a/ae/Xinghuayue2.JPG" alt="Huayue Xing" ><br />
<div class ="nameplate"><a href="#">Huayue Xing</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/a/ae/Xinghuayue2.JPG" alt="Huayue Xing" align="left"><p>With my little eyes, I see bacterium; with my little eyes, I see TD-1; with my little eyes, I see vaccine secreted out; with my little eyes, I see the future without needles. Carefulness, earnest, and a little bit of acting cute, I am XHY.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/a/ad/Xionglei1.JPG" alt="Lei Xiong"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/b/b2/Xionglei2.JPG" alt="Lei Xiong" ><br />
<div class ="nameplate"><a href="#">Lei Xiong</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/b/b2/Xionglei2.JPG" alt="Lei Xiong" align="left"><p>Despite the fact that it is me who always breaks test tubes, loses beakers, and pours reagent onto the skin of my hands, I have the enthusiasm for science. I love to explore and pursue knowledge. As long as there is a chance to see the tip of the iceberg, it doesn't matter how many test tubes I am going to break.</p><br />
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<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/7/76/Panminghao1.JPG" alt="Minghao Pan"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/e/ed/Panminghao2.JPG" alt="Minghao Pan" ><br />
<div class ="nameplate"><a href="#">Minghao Pan</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/e/ed/Panminghao2.JPG" alt="Minghao Pan" align="left"><p>I love physics and biology.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/c/c8/Dongbo1.JPG" alt="Bo Dong"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/0/08/Dongbo2.JPG" alt="Bo Dong" ><br />
<div class ="nameplate"><a href="#">Bo Dong</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/0/08/Dongbo2.JPG" alt="Bo Dong" align="left"><p>He is an earnest boy, he always work hard that every bros and sis like him, he is our team’s MVP! <br />
Hey, bro! It our pleasure to be with you!</p><br />
</div><br />
</div><br />
</div><br />
<br />
<br />
<div class ="row"><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/3/3f/Chenzhaoxiong1.JPG" alt="Zhaoxiong Chen"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/d/dc/Chenzhaoxiong2.JPG" alt="Zhaoxiong Chen"><br />
<div class ="nameplate"><a href="#">Zhaoxiong Chen</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/d/dc/Chenzhaoxiong2.JPG" alt="Zhaoxiong Chen" align="left"><p>This smart boy is good at playing all kinds of computer systems. We believe that he will refresh the history of wikis!</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/1/15/Wuming1.JPG" alt="Min Wu"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/b/be/Wuming2.JPG" alt="Min Wu"><br />
<div class ="nameplate"><a href="#">Min Wu</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/b/be/Wuming2.JPG" alt="Min Wu" align="left"><p>I love experiments. I love games. I love Weibo.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/1/14/Wangshiwei1.JPG" alt="Shiwei Wang"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/e/e2/Wangshiwei2.JPG" alt="Shiwei Wang"><br />
<div class ="nameplate"><a href="#">Shiwei Wang</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/e/e2/Wangshiwei2.JPG" alt="Shiwei Wang" align="left"><p>Again, a quiet boy is coming! He love experiment, he is Bo Dong’s loyal friend. We all believe in him, without his help we cannot achieve our goal! Thanks a lot ,my bro! </p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/e/e4/Fansijia1.JPG" alt="Sijia Fan"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/5/57/Fansijia2.JPG" alt="Sijia Fan"><br />
<div class ="nameplate"><a href="#">Sijia Fan</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/5/57/Fansijia2.JPG" alt="Sijia Fan" align="left"><p>Black humorist, and sadly, the leader is always shouting at me:” Hurry! Hurry!”.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/d/df/Pengyali1.JPG" alt="Yali Peng"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/0/0c/Pengyali2.JPG" alt="Yali Peng"><br />
<div class ="nameplate"><a href="#">Yali Peng</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/0/0c/Pengyali2.JPG" alt="Yali Peng" align="left"><p>She is a quiet girl, she likes smile, she loves doing experiments peacefully and slowly. As the best partner of Mingyue Li, every trouble become easy! Hey, little pretty we all love you! </p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/5/52/Longjie1.JPG" alt="Long jie"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/9/96/Longjie2.JPG" alt="Long jie"><br />
<div class ="nameplate"><a href="#">Long jie</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/9/96/Longjie2.JPG" alt="Long jie" align="left"><p>Clever and hard-working, I cannot agree more to do experiments with him. You never let us down!</p><br />
</div><br />
</div><br />
</div><br />
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<br />
<div class ="row"><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/b/bd/Caoqinjingwen1.JPG" alt="Cao Qinjingwen"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/f/f9/Caoqinjingwen2.JPG" alt="Cao Qinjingwen"><br />
<div class ="nameplate"><a href="#">Cao Qinjingwen</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/f/f9/Caoqinjingwen2.JPG" alt="Cao Qinjingwen" align="left"><p>Excellent! Without these kinds of words, how can I say anything to describe her? As our elder sister, she always gives us self-confident, hey soul sister!</p><br />
</div><br />
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<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/6/60/Shaoxueying1.JPG" alt="Shao Xueying"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/e/e9/Shaoxueying2.JPG" alt="Shao Xueying"><br />
<div class ="nameplate"><a href="#">Shao Xueying</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/e/e9/Shaoxueying2.JPG" alt="Shao Xueying" align="left"><p>Competent and independent, Shao Xueying has unique ideas about colors and graphics. At the same time, she is a good lecturer. She edits our wiki and make presentation for us. </p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/4/49/Qiuyanning1.JPG" alt="Yanning Qiu"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/d/df/Qiuyanning2.JPG" alt="Yanning Qiu"><br />
<div class ="nameplate"><a href="#">Yanning Qiu</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/d/df/Qiuyanning2.JPG" alt="Yanning Qiu" align="left"><p>Our little sister holds the trump cards. She always knows what do with all the words and pictures. Brave and creative, Yanning enjoys the days with new skills and knowledge. Our team was painted colorfully with the lively girl.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/9/9a/Wangzeyu1.JPG" alt="Zeyu Wang"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/5/53/Wangzeyu2.JPG" alt="Zeyu Wang"><br />
<div class ="nameplate"><a href="#">Zeyu Wang</a></div><br />
<div class = "details"><a href="http://home.ustc.edu.cn/~wangzeyu/contact%20me.htm"><img src="https://static.igem.org/mediawiki/igem.org/5/53/Wangzeyu2.JPG" alt="Zeyu Wang" align="left"></a><p>Although I was a freshman and initially came to USTC iGEM , I did some experiment in molecular cloning.I took part in human practice and wiki writing.I also helped with presentation. Thank you,USTC iGEMers!<br />
<br />
<br />
</p><br />
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<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/4/4a/Xiaozhuyun1.JPG" alt="Xiao Zhuyun"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/1/1d/Xiaozhuyun2.JPG" alt="Xiao Zhuyun"><br />
<div class ="nameplate"><a href="#">Xiao Zhuyun</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/1/1d/Xiaozhuyun2.JPG" alt="Xiao Zhuyun" align="left"><p>Sincere and straightforward, “piggy”, the curve wrecker in our eyes, puts all her efforts into research and study. Only when you get close to her, will you find that she also loves to play, that she also loves life.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/f/f3/Yanggege1.JPG" alt="Gege Yang"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/2/25/Yanggege2.JPG" alt="Gege Yang"><br />
<div class ="nameplate"><a href="#">Gege Yang</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/2/25/Yanggege2.JPG" alt="Gege Yang" align="left"><p> She is majoring in Life Sciences. She is in charge of the construction of one type of engineering bacteria producing fusion protein. As a member of the wet lab, she enjoys the work as well as meets new friends this summer.</p><br />
</div><br />
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<div class ="row"><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/6/62/Hanyingying1.JPG" alt="Han Yingying"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/c/ca/Hanyingying2.JPG" alt="Han Yingying"><br />
<div class ="nameplate"><a href="#">Han Yingying</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/c/ca/Hanyingying2.JPG" alt="Han Yingying" align="left"><p>Tender as a new-born kitty, Yingying doesn’t like to stand in the spotlight. She’s a diligent brain instead of a silken tongue. We believe that gold will shine no matter where it is. </p><br />
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<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/1/18/Chenzhuo1.JPG" alt="Chen Zhuo"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/a/af/Chenzhuo2.JPG" alt="Chen Zhuo"><br />
<div class ="nameplate"><a href="#">Chen Zhuo</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/a/af/Chenzhuo2.JPG" alt="Chen Zhuo" align="left"><p>Always, he is still of tongue, but he is not only a genius of experiment, but also a brilliant living library. I cannot say more but admire! </p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/4/4a/Xuehao1.JPG" alt="Hao Xue"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/0/08/Xuehao2.JPG" alt="Hao Xue"><br />
<div class ="nameplate"><a href="#">Hao Xue</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/0/08/Xuehao2.JPG" alt="Hao Xue" align="left"><p>Laughing, he is still laughing! What on hill? Oh god, negative results, but how… But bro, thank you for giving us positive energy, you really raise us up! My bro! </p><br />
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<h2>Advisers</h2> <br />
<div class ="profilewrap" style="width:160px;margin:60px 80px 5px;"><br />
<img src="https://static.igem.org/mediawiki/2013/f/f0/Haiyan_Liu.jpg" alt="Haiyan Liu"><br />
<div class ="nameplate"><a href="#">Haiyan Liu</a></div><br />
<div class = "details"><img style="width:160px" src="https://static.igem.org/mediawiki/2013/f/f0/Haiyan_Liu.jpg" alt="Haiyan Liu" align="left"><p>Haiyan Liu was born in Sichuan Province, China. He received his BS degree in Biology in 1990 and PhD degree in Biochemistry and Molecular Biology in 1996, both from USTC. Between 1993 and 1995 he was a visiting graduate student in Laboratory of Physical Chemistry of ETH, Zurich (Switzerland). Since 2001, he has been a professor of computational biology at School of Life Sciences, USTC. </p><br />
</div><br />
</div><br />
<div class ="profilewrap" style="width:160px;margin:60px 80px 5px;"><br />
<img src="https://static.igem.org/mediawiki/2013/0/09/Hongjiong.PNG" alt="Jiong Hong"><br />
<div class ="nameplate"><a href="#">Jiong Hong</a></div><br />
<div class = "details"><img style="width:160px" src="https://static.igem.org/mediawiki/2013/0/09/Hongjiong.PNG" alt="Jiong Hong" align="left"><p> I am applying this strategy on the mechanism of the complex diseases such as cancer and diabetes. My ongoing project is to identify biomarkers in order to detect the progress stages of the diabetes. In addition, I have planed to analyze the genetic and environmental factors and their interactions during the progressing of the type 2 diabetes with systems-biology approaches.</p><br />
</div><br />
</div><br />
<div class ="profilewrap" style="width:160px;margin:60px 80px 5px;"><br />
<img src="https://static.igem.org/mediawiki/2013/4/46/Wujiarui.PNG" alt="Jiarui Wu"><br />
<div class ="nameplate"><a href="#">Jiarui Wu</a></div><br />
<div class = "details"><img style="width:160px" src="https://static.igem.org/mediawiki/2013/4/46/Wujiarui.PNG" alt="Jiarui Wu" align="left"><p>Since the research strategy of systems biology is well fit to analyze the biological complex systems. I am applying this strategy on the mechanism of the complex diseases such as cancer and diabetes. We have developed systematic approaches based on proteomics and bioinformatics to analyze human normal and diabetic serum.</p><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC_CHINA/TeamTeam:USTC CHINA/Team2013-09-27T15:32:15Z<p>USTCkun: </p>
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<h2>Students</h2><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/2/21/Zhangsitao1.JPG" alt="Zhang Sitao"> <br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/0/0d/Zhangsitao2.JPG" alt="Zhang Sitao"><br />
<div class ="nameplate"><a href="#">Zhang Sitao</a> </div><br />
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<img src="https://static.igem.org/mediawiki/2013/0/0d/Zhangsitao2.JPG" alt="Zhang Sitao" align="left"><p>Our labor leader weighs various matters, leads the overall trend and plays our cards right. He leaves a strong impression in others’ mind. However, His friends found that the leader is very cute.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/0/02/Zhaochanglong1.JPG" alt="Changlong Zhao"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/7/7e/Zhaochanglong2.JPG" alt="Changlong Zhao" ><br />
<div class ="nameplate"><a href="#">Changlong Zhao</a></div><br />
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<img src="https://static.igem.org/mediawiki/2013/7/7e/Zhaochanglong2.JPG" alt="Changlong Zhao" align="left"><p>There’s no doubt that we can give full stars for Changlong’s fighting capacity. The roads leading to success will never be smooth and Changlong is a perfect companion to travel with.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/9/91/Xionghanjin1.JPG" alt="Hanjin Xiong"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/d/d9/Xionghanjin2.JPG" alt="Hanjin Xiong" ><br />
<div class ="nameplate"><a href="#">Hanjin Xiong</a></div><br />
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<img src="https://static.igem.org/mediawiki/2013/d/d9/Xionghanjin2.JPG" alt="Hanjin Xiong" align="left"><p>As the keynote speaker of our team, he always keeps a clear head with extraordinary creativity and expressiveness. He said, “It is shameful if you haven’t burnt the midnight oil for iGEM.” Moving forward bravely, he shows us overwhelming power which nobody can stop it.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/2/23/Limingyue1.JPG" alt="Mingyue Li"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/0/05/Limingyue2.JPG" alt="Mingyue Li"><br />
<div class ="nameplate"><a href="#">Mingyue Li</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/0/05/Limingyue2.JPG" alt="Mingyue Li" align="left"><p>“Mingyue Bacteria” is the spokesperson of our bacterium, handling the destiny of those little lives. We all agreed that, Mingyue with rubber gloves is GORGEOUSNESS!</p></div><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/8/85/Shenshengqi1.JPG" alt="Shen Shengqi"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/4/44/Shenshengqi2.JPG" alt="Shen Shengqi" ><br />
<div class ="nameplate"><a href="#">Shen Shengqi</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/4/44/Shenshengqi2.JPG" alt="Shen Shengqi" align="left"><p>Everyone considers that it is honored to be a friend of “Brother Face” as he is a totally “local tyrant”. Actually,” Brother Shen” is warmth, nice, really expert in digging shortcuts in the experiments. He is a sharp soldier of our team as he adheres to the “more with less” principle.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/c/c1/Madanyi1.JPG" alt="Danyi Ma"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/1/19/Madanyi2.JPG" alt="Danyi Ma" ><br />
<div class ="nameplate"><a href="#">Danyi Ma</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/1/19/Madanyi2.JPG" alt="Danyi Ma" align="left"><p>”Aunty Ma” takes charge of our finance and safety, which calls for much patience and responsibility. In the experiment, she also plays an absolutely necessary role.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/8/87/Zhangheng1.JPG" alt="Zhang Heng"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/6/63/Zhangheng2.JPG" alt="Zhang Heng" ><br />
<div class ="nameplate"><a href="#">Zhang Heng</a> </div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/6/63/Zhangheng2.JPG" alt="Zhang Heng" align="left"><p>He is a man full of responsibility, we could 100% trust him! Bro, it’s you that bring us positive energy!</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/f/f9/Yuanye1.JPG" alt="Yvette Yuan"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/8/81/Yuanye2.JPG" alt="Yvette Yuan" ><br />
<div class ="nameplate"><a href="#">Yvette Yuan</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/8/81/Yuanye2.JPG" alt="Yvette Yuan" align="left"><p>As a s pronoun for efficient, Yuan Ye is studious and decisive. And what makes her best is that she always brings us delicious oranges.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/a/a3/Xinghuayue1.JPG" alt="Huayue Xing"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/a/ae/Xinghuayue2.JPG" alt="Huayue Xing" ><br />
<div class ="nameplate"><a href="#">Huayue Xing</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/a/ae/Xinghuayue2.JPG" alt="Huayue Xing" align="left"><p>With my little eyes, I see bacterium; with my little eyes, I see TD-1; with my little eyes, I see vaccine secreted out; with my little eyes, I see the future without needles. Carefulness, earnest, and a little bit of acting cute, I am XHY.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/a/ad/Xionglei1.JPG" alt="Lei Xiong"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/b/b2/Xionglei2.JPG" alt="Lei Xiong" ><br />
<div class ="nameplate"><a href="#">Lei Xiong</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/b/b2/Xionglei2.JPG" alt="Lei Xiong" align="left"><p>Despite the fact that it is me who always breaks test tubes, loses beakers, and pours reagent onto the skin of my hands, I have the enthusiasm for science. I love to explore and pursue knowledge. As long as there is a chance to see the tip of the iceberg, it doesn't matter how many test tubes I am going to break.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/7/76/Panminghao1.JPG" alt="Minghao Pan"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/e/ed/Panminghao2.JPG" alt="Minghao Pan" ><br />
<div class ="nameplate"><a href="#">Minghao Pan</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/e/ed/Panminghao2.JPG" alt="Minghao Pan" align="left"><p>I love physics and biology.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/c/c8/Dongbo1.JPG" alt="Bo Dong"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/0/08/Dongbo2.JPG" alt="Bo Dong" ><br />
<div class ="nameplate"><a href="#">Bo Dong</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/0/08/Dongbo2.JPG" alt="Bo Dong" align="left"><p>He is an earnest boy, he always work hard that every bros and sis like him, he is our team’s MVP! <br />
Hey, bro! It our pleasure to be with you!</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/3/3f/Chenzhaoxiong1.JPG" alt="Zhaoxiong Chen"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/d/dc/Chenzhaoxiong2.JPG" alt="Zhaoxiong Chen"><br />
<div class ="nameplate"><a href="#">Zhaoxiong Chen</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/d/dc/Chenzhaoxiong2.JPG" alt="Zhaoxiong Chen" align="left"><p>This smart boy is good at playing all kinds of computer systems. We believe that he will refresh the history of wikis!</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/1/15/Wuming1.JPG" alt="Min Wu"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/b/be/Wuming2.JPG" alt="Min Wu"><br />
<div class ="nameplate"><a href="#">Min Wu</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/b/be/Wuming2.JPG" alt="Min Wu" align="left"><p>I love experiments. I love games. I love Weibo.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/1/14/Wangshiwei1.JPG" alt="Shiwei Wang"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/e/e2/Wangshiwei2.JPG" alt="Shiwei Wang"><br />
<div class ="nameplate"><a href="#">Shiwei Wang</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/e/e2/Wangshiwei2.JPG" alt="Shiwei Wang" align="left"><p>Again, a quiet boy is coming! He love experiment, he is Bo Dong’s loyal friend. We all believe in him, without his help we cannot achieve our goal! Thanks a lot ,my bro! </p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/2013/e/e4/Fansijia1.JPG" alt="Sijia Fan"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/5/57/Fansijia2.JPG" alt="Sijia Fan"><br />
<div class ="nameplate"><a href="#">Sijia Fan</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/5/57/Fansijia2.JPG" alt="Sijia Fan" align="left"><p>Black humorist, and sadly, the leader is always shouting at me:” Hurry! Hurry!”.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/d/df/Pengyali1.JPG" alt="Yali Peng"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/0/0c/Pengyali2.JPG" alt="Yali Peng"><br />
<div class ="nameplate"><a href="#">Yali Peng</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/0/0c/Pengyali2.JPG" alt="Yali Peng" align="left"><p>She is a quiet girl, she likes smile, she loves doing experiments peacefully and slowly. As the best partner of Mingyue Li, every trouble become easy! Hey, little pretty we all love you! </p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/5/52/Longjie1.JPG" alt="Long jie"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/9/96/Longjie2.JPG" alt="Long jie"><br />
<div class ="nameplate"><a href="#">Long jie</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/9/96/Longjie2.JPG" alt="Long jie" align="left"><p>Clever and hard-working, I cannot agree more to do experiments with him. You never let us down!</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/b/bd/Caoqinjingwen1.JPG" alt="Cao Qinjingwen"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/f/f9/Caoqinjingwen2.JPG" alt="Cao Qinjingwen"><br />
<div class ="nameplate"><a href="#">Cao Qinjingwen</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/f/f9/Caoqinjingwen2.JPG" alt="Cao Qinjingwen" align="left"><p>Excellent! Without these kinds of words, how can I say anything to describe her? As our elder sister, she always gives us self-confident, hey soul sister!</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/6/60/Shaoxueying1.JPG" alt="Shao Xueying"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/e/e9/Shaoxueying2.JPG" alt="Shao Xueying"><br />
<div class ="nameplate"><a href="#">Shao Xueying</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/e/e9/Shaoxueying2.JPG" alt="Shao Xueying" align="left"><p>Competent and independent, Shao Xueying has unique ideas about colors and graphics. At the same time, she is a good lecturer. She edits our wiki and make presentation for us. </p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/4/49/Qiuyanning1.JPG" alt="Yanning Qiu"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/d/df/Qiuyanning2.JPG" alt="Yanning Qiu"><br />
<div class ="nameplate"><a href="#">Yanning Qiu</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/d/df/Qiuyanning2.JPG" alt="Yanning Qiu" align="left"><p>Our little sister holds the trump cards. She always knows what do with all the words and pictures. Brave and creative, Yanning enjoys the days with new skills and knowledge. Our team was painted colorfully with the lively girl.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/9/9a/Wangzeyu1.JPG" alt="Zeyu Wang"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/5/53/Wangzeyu2.JPG" alt="Zeyu Wang"><br />
<div class ="nameplate"><a href="#">Zeyu Wang</a></div><br />
<div class = "details"><a href="http://home.ustc.edu.cn/~wangzeyu/contact%20me.htm"><img src="https://static.igem.org/mediawiki/igem.org/5/53/Wangzeyu2.JPG" alt="Zeyu Wang" align="left"></a><p>Although I was a freshman and initially came to USTC iGEM , I did some experiment in molecular cloning.I took part in human practice and wiki writing.I also helped with presentation. Thank you,USTC iGEMers!<br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/4/4a/Xiaozhuyun1.JPG" alt="Xiao Zhuyun"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/1/1d/Xiaozhuyun2.JPG" alt="Xiao Zhuyun"><br />
<div class ="nameplate"><a href="#">Xiao Zhuyun</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/1/1d/Xiaozhuyun2.JPG" alt="Xiao Zhuyun" align="left"><p>Sincere and straightforward, “piggy”, the curve wrecker in our eyes, puts all her efforts into research and study. Only when you get close to her, will you find that she also loves to play, that she also loves life.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/f/f3/Yanggege1.JPG" alt="Gege Yang"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/2/25/Yanggege2.JPG" alt="Gege Yang"><br />
<div class ="nameplate"><a href="#">Gege Yang</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/2/25/Yanggege2.JPG" alt="Gege Yang" align="left"><p> Yang Gege is majoring in Life Sciences. She is in charge of the construction of one type of engineering bacteria producing fusion protein. As a member of the wet lab, she enjoys the work as well as meets new friends this summer.</p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/6/62/Hanyingying1.JPG" alt="Han Yingying"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/c/ca/Hanyingying2.JPG" alt="Han Yingying"><br />
<div class ="nameplate"><a href="#">Han Yingying</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/c/ca/Hanyingying2.JPG" alt="Han Yingying" align="left"><p>Tender as a new-born kitty, Yingying doesn’t like to stand in the spotlight. She’s a diligent brain instead of a silken tongue. We believe that gold will shine no matter where it is. </p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/1/18/Chenzhuo1.JPG" alt="Chen Zhuo"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/a/af/Chenzhuo2.JPG" alt="Chen Zhuo"><br />
<div class ="nameplate"><a href="#">Chen Zhuo</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/a/af/Chenzhuo2.JPG" alt="Chen Zhuo" align="left"><p>Always, he is still of tongue, but he is not only a genius of experiment, but also a brilliant living library. I cannot say more but admire! </p><br />
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<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/4/4a/Xuehao1.JPG" alt="Hao Xue"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/0/08/Xuehao2.JPG" alt="Hao Xue"><br />
<div class ="nameplate"><a href="#">Hao Xue</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/0/08/Xuehao2.JPG" alt="Hao Xue" align="left"><p>Laughing, he is still laughing! What on hill? Oh god, negative results, but how… But bro, thank you for giving us positive energy, you really raise us up! My bro! </p><br />
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<h2>Advisers</h2> <br />
<div class ="profilewrap" style="width:160px;margin:60px 80px 5px;"><br />
<img src="https://static.igem.org/mediawiki/2013/f/f0/Haiyan_Liu.jpg" alt="Haiyan Liu"><br />
<div class ="nameplate"><a href="#">Haiyan Liu</a></div><br />
<div class = "details"><img style="width:160px" src="https://static.igem.org/mediawiki/2013/f/f0/Haiyan_Liu.jpg" alt="Haiyan Liu" align="left"><p>Haiyan Liu was born in Sichuan Province, China. He received his BS degree in Biology in 1990 and PhD degree in Biochemistry and Molecular Biology in 1996, both from USTC. Between 1993 and 1995 he was a visiting graduate student in Laboratory of Physical Chemistry of ETH, Zurich (Switzerland). Since 2001, he has been a professor of computational biology at School of Life Sciences, USTC. </p><br />
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<div class ="profilewrap" style="width:160px;margin:60px 80px 5px;"><br />
<img src="https://static.igem.org/mediawiki/2013/0/09/Hongjiong.PNG" alt="Jiong Hong"><br />
<div class ="nameplate"><a href="#">Jiong Hong</a></div><br />
<div class = "details"><img style="width:160px" src="https://static.igem.org/mediawiki/2013/0/09/Hongjiong.PNG" alt="Jiong Hong" align="left"><p> I am applying this strategy on the mechanism of the complex diseases such as cancer and diabetes. My ongoing project is to identify biomarkers in order to detect the progress stages of the diabetes. In addition, I have planed to analyze the genetic and environmental factors and their interactions during the progressing of the type 2 diabetes with systems-biology approaches.</p><br />
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<img src="https://static.igem.org/mediawiki/2013/4/46/Wujiarui.PNG" alt="Jiarui Wu"><br />
<div class ="nameplate"><a href="#">Jiarui Wu</a></div><br />
<div class = "details"><img style="width:160px" src="https://static.igem.org/mediawiki/2013/4/46/Wujiarui.PNG" alt="Jiarui Wu" align="left"><p>Since the research strategy of systems biology is well fit to analyze the biological complex systems. I am applying this strategy on the mechanism of the complex diseases such as cancer and diabetes. We have developed systematic approaches based on proteomics and bioinformatics to analyze human normal and diabetic serum.</p><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC_CHINA/TeamTeam:USTC CHINA/Team2013-09-27T15:28:33Z<p>USTCkun: </p>
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<div id="igemlogo"><a href="https://2013.igem.org/Main_Page" target="_blank"><img src="https://static.igem.org/mediawiki/2013/2/26/2013ustcigem_IGEM_basic_Logo.png" alt="igem home page" width="50" height="40" /></a></div><br />
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<li><a href="https://2013.igem.org/Team:USTC_CHINA">Home</a></li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/Project/Overview">Project</a><br />
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<li><a href="https://2013.igem.org/Team:USTC_CHINA/Project/Overview">Overview</a></li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/Project/ProjectDetails">Project Details</a></li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/Project/Results">Results</a></li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/Parts">Parts</a></li><br />
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<li><a href="https://2013.igem.org/Team:USTC_CHINA/Notebook">Notebook</a><br />
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<li><a href="https://2013.igem.org/Team:USTC_CHINA/Notebook/Timeline">Timeline</a></li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/Notebook/Protocols">Protocols</a></li><br />
</ul><br />
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<li><a href="https://2013.igem.org/Team:USTC_CHINA/Modeling/">Modeling</a><br />
<ul class="subs"><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/Modeling/KillSwitch">Kill Switch</a></li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/Modeling/B.SubtilisCulture">B.Subtilis Culture</a></li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/Modeling/DesignsofImmuneExperiments">Designs of Immune Experiments</a></li><br />
</ul><br />
</li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/HumanPractice">Human Practice</a><br />
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<li><a href="https://2013.igem.org/Team:USTC_CHINA/HumanPractice/Communication" >Communication</a></li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/HumanPractice/Activity">Activity</a></li><br />
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<li class="active"><a href="https://2013.igem.org/Team:USTC_CHINA/Team">Team</a><br />
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<li><a href="https://2013.igem.org/Team:USTC_CHINA/Team">Members</a></li><br />
<li><a href="https://igem.org/Team.cgi?year=2013&team_name=USTC_CHINA">Profile</a></li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/Attributions">Attributions</a></li><br />
<li><a href="https://igem.org/2013_Judging_Form?id=1074#iGEM_Medals">Achievements</a></li><br />
</ul><br />
</li><br />
<li><a href="https://2013.igem.org/Team:USTC_CHINA/Safety">Safety</a></li><br />
</ul><br />
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<div class="content" align="center" style="margin-bottom: 50px"><br />
<h2>Students</h2><br />
<div class ="row"><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/2/21/Zhangsitao1.JPG" alt="Zhang Sitao"> <br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/0/0d/Zhangsitao2.JPG" alt="Zhang Sitao"><br />
<div class ="nameplate"><a href="#">Zhang Sitao</a> </div><br />
<div class = "details"><br />
<img src="https://static.igem.org/mediawiki/2013/0/0d/Zhangsitao2.JPG" alt="Zhang Sitao" align="left"><p>Our labor leader weighs various matters, leads the overall trend and plays our cards right. He leaves a strong impression in others’ mind. However, His friends found that the leader is very cute.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/0/02/Zhaochanglong1.JPG" alt="Changlong Zhao"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/7/7e/Zhaochanglong2.JPG" alt="Changlong Zhao" ><br />
<div class ="nameplate"><a href="#">Changlong Zhao</a></div><br />
<div class = "details"><br />
<img src="https://static.igem.org/mediawiki/2013/7/7e/Zhaochanglong2.JPG" alt="Changlong Zhao" align="left"><p>There’s no doubt that we can give full stars for Changlong’s fighting capacity. The roads leading to success will never be smooth and Changlong is a perfect companion to travel with.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/9/91/Xionghanjin1.JPG" alt="Hanjin Xiong"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/d/d9/Xionghanjin2.JPG" alt="Hanjin Xiong" ><br />
<div class ="nameplate"><a href="#">Hanjin Xiong</a></div><br />
<div class = "details"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d9/Xionghanjin2.JPG" alt="Hanjin Xiong" align="left"><p>As the keynote speaker of our team, he always keeps a clear head with extraordinary creativity and expressiveness. He said, “It is shameful if you haven’t burnt the midnight oil for iGEM.” Moving forward bravely, he shows us overwhelming power which nobody can stop it.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/2/23/Limingyue1.JPG" alt="Mingyue Li"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/0/05/Limingyue2.JPG" alt="Mingyue Li"><br />
<div class ="nameplate"><a href="#">Mingyue Li</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/0/05/Limingyue2.JPG" alt="Mingyue Li" align="left"><p>“Mingyue Bacteria” is the spokesperson of our bacterium, handling the destiny of those little lives. We all agreed that, Mingyue with rubber gloves is GORGEOUSNESS!</p></div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/8/85/Shenshengqi1.JPG" alt="Shen Shengqi"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/4/44/Shenshengqi2.JPG" alt="Shen Shengqi" ><br />
<div class ="nameplate"><a href="#">Shen Shengqi</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/4/44/Shenshengqi2.JPG" alt="Shen Shengqi" align="left"><p>Everyone considers that it is honored to be a friend of “Brother Face” as he is a totally “local tyrant”. Actually,” Brother Shen” is warmth, nice, really expert in digging shortcuts in the experiments. He is a sharp soldier of our team as he adheres to the “more with less” principle.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/c/c1/Madanyi1.JPG" alt="Danyi Ma"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/1/19/Madanyi2.JPG" alt="Danyi Ma" ><br />
<div class ="nameplate"><a href="#">Danyi Ma</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/1/19/Madanyi2.JPG" alt="Danyi Ma" align="left"><p>”Aunty Ma” takes charge of our finance and safety, which calls for much patience and responsibility. In the experiment, she also plays an absolutely necessary role.</p><br />
</div><br />
</div><br />
</div><br />
<br />
<br />
<div class ="row"><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/8/87/Zhangheng1.JPG" alt="Zhang Heng"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/6/63/Zhangheng2.JPG" alt="Zhang Heng" ><br />
<div class ="nameplate"><a href="#">Zhang Heng</a> </div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/6/63/Zhangheng2.JPG" alt="Zhang Heng" align="left"><p>He is a man full of responsibility, we could 100% trust him! Bro, it’s you that bring us positive energy!</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/f/f9/Yuanye1.JPG" alt="Yvette Yuan"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/8/81/Yuanye2.JPG" alt="Yvette Yuan" ><br />
<div class ="nameplate"><a href="#">Yvette Yuan</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/8/81/Yuanye2.JPG" alt="Yvette Yuan" align="left"><p>As a s pronoun for efficient, Yuan Ye is studious and decisive. And what makes her best is that she always brings us delicious oranges.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/a/a3/Xinghuayue1.JPG" alt="Huayue Xing"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/a/ae/Xinghuayue2.JPG" alt="Huayue Xing" ><br />
<div class ="nameplate"><a href="#">Huayue Xing</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/a/ae/Xinghuayue2.JPG" alt="Huayue Xing" align="left"><p>With my little eyes, I see bacterium; with my little eyes, I see TD-1; with my little eyes, I see vaccine secreted out; with my little eyes, I see the future without needles. Carefulness, earnest, and a little bit of acting cute, I am XHY.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/a/ad/Xionglei1.JPG" alt="Lei Xiong"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/b/b2/Xionglei2.JPG" alt="Lei Xiong" ><br />
<div class ="nameplate"><a href="#">Lei Xiong</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/b/b2/Xionglei2.JPG" alt="Lei Xiong" align="left"><p>Despite the fact that it is me who always breaks test tubes, loses beakers, and pours reagent onto the skin of my hands, I have the enthusiasm for science. I love to explore and pursue knowledge. As long as there is a chance to see the tip of the iceberg, it doesn't matter how many test tubes I am going to break.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/7/76/Panminghao1.JPG" alt="Minghao Pan"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/e/ed/Panminghao2.JPG" alt="Minghao Pan" ><br />
<div class ="nameplate"><a href="#">Minghao Pan</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/e/ed/Panminghao2.JPG" alt="Minghao Pan" align="left"><p>I love physics and biology.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/c/c8/Dongbo1.JPG" alt="Bo Dong"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/0/08/Dongbo2.JPG" alt="Bo Dong" ><br />
<div class ="nameplate"><a href="#">Bo Dong</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/0/08/Dongbo2.JPG" alt="Bo Dong" align="left"><p>He is an earnest boy, he always work hard that every bros and sis like him, he is our team’s MVP! <br />
Hey, bro! It our pleasure to be with you!</p><br />
</div><br />
</div><br />
</div><br />
<br />
<br />
<div class ="row"><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/3/3f/Chenzhaoxiong1.JPG" alt="Zhaoxiong Chen"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/d/dc/Chenzhaoxiong2.JPG" alt="Zhaoxiong Chen"><br />
<div class ="nameplate"><a href="#">Zhaoxiong Chen</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/d/dc/Chenzhaoxiong2.JPG" alt="Zhaoxiong Chen" align="left"><p>This smart boy is good at playing all kinds of computer systems. We believe that he will refresh the history of wikis!</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/1/15/Wuming1.JPG" alt="Min Wu"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/b/be/Wuming2.JPG" alt="Min Wu"><br />
<div class ="nameplate"><a href="#">Min Wu</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/b/be/Wuming2.JPG" alt="Min Wu" align="left"><p>I love experiments. I love games. I love Weibo.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/1/14/Wangshiwei1.JPG" alt="Shiwei Wang"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/e/e2/Wangshiwei2.JPG" alt="Shiwei Wang"><br />
<div class ="nameplate"><a href="#">Shiwei Wang</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/e/e2/Wangshiwei2.JPG" alt="Shiwei Wang" align="left"><p>Again, a quiet boy is coming! He love experiment, he is Bo Dong’s loyal friend. We all believe in him, without his help we cannot achieve our goal! Thanks a lot ,my bro! </p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/2013/e/e4/Fansijia1.JPG" alt="Sijia Fan"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/2013/5/57/Fansijia2.JPG" alt="Sijia Fan"><br />
<div class ="nameplate"><a href="#">Sijia Fan</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/2013/5/57/Fansijia2.JPG" alt="Sijia Fan" align="left"><p>Black humorist, and sadly, the leader is always shouting at me:” Hurry! Hurry!”.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/d/df/Pengyali1.JPG" alt="Yali Peng"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/0/0c/Pengyali2.JPG" alt="Yali Peng"><br />
<div class ="nameplate"><a href="#">Yali Peng</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/0/0c/Pengyali2.JPG" alt="Yali Peng" align="left"><p>She is a quiet girl, she likes smile, she loves doing experiments peacefully and slowly. As the best partner of Mingyue Li, every trouble become easy! Hey, little pretty we all love you! </p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/5/52/Longjie1.JPG" alt="Long jie"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/9/96/Longjie2.JPG" alt="Long jie"><br />
<div class ="nameplate"><a href="#">Long jie</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/9/96/Longjie2.JPG" alt="Long jie" align="left"><p>Clever and hard-working, I cannot agree more to do experiments with him. You never let us down!</p><br />
</div><br />
</div><br />
</div><br />
<br />
<br />
<div class ="row"><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/b/bd/Caoqinjingwen1.JPG" alt="Cao Qinjingwen"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/f/f9/Caoqinjingwen2.JPG" alt="Cao Qinjingwen"><br />
<div class ="nameplate"><a href="#">Cao Qinjingwen</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/f/f9/Caoqinjingwen2.JPG" alt="Cao Qinjingwen" align="left"><p>Excellent! Without these kinds of words, how can I say anything to describe her? As our elder sister, she always gives us self-confident, hey soul sister!</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/6/60/Shaoxueying1.JPG" alt="Shao Xueying"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/e/e9/Shaoxueying2.JPG" alt="Shao Xueying"><br />
<div class ="nameplate"><a href="#">Shao Xueying</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/e/e9/Shaoxueying2.JPG" alt="Shao Xueying" align="left"><p>Competent and independent, Shao Xueying has unique ideas about colors and graphics. At the same time, she is a good lecturer. She edits our wiki for us. </p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/4/49/Qiuyanning1.JPG" alt="Yanning Qiu"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/d/df/Qiuyanning2.JPG" alt="Yanning Qiu"><br />
<div class ="nameplate"><a href="#">Yanning Qiu</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/d/df/Qiuyanning2.JPG" alt="Yanning Qiu" align="left"><p>Our little sister holds the trump cards. She always knows what do with all the words and pictures. Brave and creative, Yanning enjoys the days with new skills and knowledge. Our team was painted colorfully with the lively girl.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/9/9a/Wangzeyu1.JPG" alt="Zeyu Wang"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/5/53/Wangzeyu2.JPG" alt="Zeyu Wang"><br />
<div class ="nameplate"><a href="#">Zeyu Wang</a></div><br />
<div class = "details"><a href="http://home.ustc.edu.cn/~wangzeyu/contact%20me.htm"><img src="https://static.igem.org/mediawiki/igem.org/5/53/Wangzeyu2.JPG" alt="Zeyu Wang" align="left"></a><p>Although I was a freshman and initially came to USTC iGEM , I did some experiment in molecular cloning.I took part in human practice and wiki writing.I also helped with presentation. Thank you,USTC iGEMers!<br />
<br />
<br />
</p><br />
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<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/4/4a/Xiaozhuyun1.JPG" alt="Xiao Zhuyun"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/1/1d/Xiaozhuyun2.JPG" alt="Xiao Zhuyun"><br />
<div class ="nameplate"><a href="#">Xiao Zhuyun</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/1/1d/Xiaozhuyun2.JPG" alt="Xiao Zhuyun" align="left"><p>Sincere and straightforward, “piggy”, the curve wrecker in our eyes, puts all her efforts into research and study. Only when you get close to her, will you find that she also loves to play, that she also loves life.</p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/f/f3/Yanggege1.JPG" alt="Gege Yang"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/2/25/Yanggege2.JPG" alt="Gege Yang"><br />
<div class ="nameplate"><a href="#">Gege Yang</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/2/25/Yanggege2.JPG" alt="Yang Gege" align="left"><p> Yang Gege is majoring in Life Sciences. She is in charge of the construction of one type of engineering bacteria producing fusion protein. As a member of the wet lab, she enjoys the work as well as meets new friends this summer.</p><br />
</div><br />
</div><br />
</div><br />
<br />
<br />
<div class ="row"><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/6/62/Hanyingying1.JPG" alt="Han Yingying"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/c/ca/Hanyingying2.JPG" alt="Han Yingying"><br />
<div class ="nameplate"><a href="#">Han Yingying</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/c/ca/Hanyingying2.JPG" alt="Han Yingying" align="left"><p>Tender as a new-born kitty, Yingying doesn’t like to stand in the spotlight. She’s a diligent brain instead of a silken tongue. We believe that gold will shine no matter where it is. </p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/1/18/Chenzhuo1.JPG" alt="Chen Zhuo"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/a/af/Chenzhuo2.JPG" alt="Chen Zhuo"><br />
<div class ="nameplate"><a href="#">Chen Zhuo</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/a/af/Chenzhuo2.JPG" alt="Chen Zhuo" align="left"><p>Always, he is still of tongue, but he is not only a genius of experiment, but also a brilliant living library. I cannot say more but admire! </p><br />
</div><br />
</div><br />
<div class ="profilewrap"><br />
<img class ="normal" src="https://static.igem.org/mediawiki/igem.org/4/4a/Xuehao1.JPG" alt="Hao Xue"><br />
<img class ="crazy" src="https://static.igem.org/mediawiki/igem.org/0/08/Xuehao2.JPG" alt="Hao Xue"><br />
<div class ="nameplate"><a href="#">Hao Xue</a></div><br />
<div class = "details"><img src="https://static.igem.org/mediawiki/igem.org/0/08/Xuehao2.JPG" alt="Hao Xue" align="left"><p>Laughing, he is still laughing! What on hill? Oh god, negative results, but how… But bro, thank you for giving us positive energy, you really raise us up! My bro! </p><br />
</div><br />
</div><br />
</</div><br />
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<h2>Advisers</h2> <br />
<div class ="profilewrap" style="width:160px;margin:60px 80px 5px;"><br />
<img src="https://static.igem.org/mediawiki/2013/f/f0/Haiyan_Liu.jpg" alt="Haiyan Liu"><br />
<div class ="nameplate"><a href="#">Haiyan Liu</a></div><br />
<div class = "details"><img style="width:160px" src="https://static.igem.org/mediawiki/2013/f/f0/Haiyan_Liu.jpg" alt="Haiyan Liu" align="left"><p>Haiyan Liu was born in Sichuan Province, China. He received his BS degree in Biology in 1990 and PhD degree in Biochemistry and Molecular Biology in 1996, both from USTC. Between 1993 and 1995 he was a visiting graduate student in Laboratory of Physical Chemistry of ETH, Zurich (Switzerland). Since 2001, he has been a professor of computational biology at School of Life Sciences, USTC. </p><br />
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<div class ="profilewrap" style="width:160px;margin:60px 80px 5px;"><br />
<img src="https://static.igem.org/mediawiki/2013/0/09/Hongjiong.PNG" alt="Jiong Hong"><br />
<div class ="nameplate"><a href="#">Jiong Hong</a></div><br />
<div class = "details"><img style="width:160px" src="https://static.igem.org/mediawiki/2013/0/09/Hongjiong.PNG" alt="Jiong Hong" align="left"><p> I am applying this strategy on the mechanism of the complex diseases such as cancer and diabetes. My ongoing project is to identify biomarkers in order to detect the progress stages of the diabetes. In addition, I have planed to analyze the genetic and environmental factors and their interactions during the progressing of the type 2 diabetes with systems-biology approaches.</p><br />
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<div class ="profilewrap" style="width:160px;margin:60px 80px 5px;"><br />
<img src="https://static.igem.org/mediawiki/2013/4/46/Wujiarui.PNG" alt="Jiarui Wu"><br />
<div class ="nameplate"><a href="#">Jiarui Wu</a></div><br />
<div class = "details"><img style="width:160px" src="https://static.igem.org/mediawiki/2013/4/46/Wujiarui.PNG" alt="Jiarui Wu" align="left"><p>Since the research strategy of systems biology is well fit to analyze the biological complex systems. I am applying this strategy on the mechanism of the complex diseases such as cancer and diabetes. We have developed systematic approaches based on proteomics and bioinformatics to analyze human normal and diabetic serum.</p><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/ExamplesTeam:USTC-Software/Project/Examples2013-09-26T15:32:57Z<p>USTCkun: </p>
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<h1>Examples</h1><br />
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<p align="justify">To prove our software’s reliability, we search for literatures to test our program. It is hard to find an appropriate literature which studies the effect of importing an exogenous gene into E.coli K-12. But actually, our software could also simulate the effect of changing endogenous gene by putting the same promoter and gene sequence in.</p><br />
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<br />
<p align="justify">In the literature we found, Stuart and his team measured the gene expression profiles in otherwise isogenic integration host factor IHF+ and IHF- strains. And IHF is one of the genes in our genetic regulatory network(GRN). </p><br />
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<p align="justify">By importing the IHF’s promoter and gene sequence, we used our software simulating the enhancement of IHF’s expression and compared the result with the gene expression profile in that literature.</p><br />
<br />
<p align="justify">There are 30 genes in that profile which are also in our GRN. Here is the list and Genes differentially expressed between E. coli K12 strains IH100 (IHF+) and IH105 (IHF-) with a p value less than 0.0005:</p><br />
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<table width="1000px" border="0" cellspacing="0" cellpadding="0" style="border-top:2px #000000 solid;border-bottom:2px #000000 solid;"><br />
<tr><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Gene</p></td><br />
<td width="137" colspan="2" style="border-bottom:2px #000 solid;"><p align="center">Avg</p></td><br />
<td width="137" colspan="2" style="border-bottom:2px #000 solid;"><p align="center">S.D.</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">p value</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Fold</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Compare<br /><br />
Result</p></td><br />
</tr><br />
<tr><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH100</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH105</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH100</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH105</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>glnA</em></p></td><br />
<td width="69"><p align="center">2.91E-03</p></td><br />
<td width="69"><p align="center">9.39E-04</p></td><br />
<td width="69"><p align="center">6.80E-04</p></td><br />
<td width="69"><p align="center">1.33E-04</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">-3.1</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvA</em></p></td><br />
<td width="69"><p align="center">5.06E-04</p></td><br />
<td width="69"><p align="center">3.42E-04</p></td><br />
<td width="69"><p align="center">1.86E-05</p></td><br />
<td width="69"><p align="center">2.26E-05</p></td><br />
<td width="69"><p align="center">3.00E-05</p></td><br />
<td width="69"><p align="center">-1.48</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvE</em></p></td><br />
<td width="69"><p align="center">5.81E-04</p></td><br />
<td width="69"><p align="center">3.58E-04</p></td><br />
<td width="69"><p align="center">4.70E-05</p></td><br />
<td width="69"><p align="center">5.77E-05</p></td><br />
<td width="69"><p align="center">9.80E-04</p></td><br />
<td width="69"><p align="center">-1.62</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvG</em></p></td><br />
<td width="69"><p align="center">1.97E-04</p></td><br />
<td width="69"><p align="center">7.67E-05</p></td><br />
<td width="69"><p align="center">2.65E-05</p></td><br />
<td width="69"><p align="center">2.23E-05</p></td><br />
<td width="69"><p align="center">4.40E-04</p></td><br />
<td width="69"><p align="center">-2.57</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>leuA</em></p></td><br />
<td width="69"><p align="center">6.99E-04</p></td><br />
<td width="69"><p align="center">1.07E-03</p></td><br />
<td width="69"><p align="center">9.21E-05</p></td><br />
<td width="69"><p align="center">9.23E-05</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">1.53</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cobT</em></p></td><br />
<td width="69"><p align="center">1.00E-05</p></td><br />
<td width="69"><p align="center">7.97E-05</p></td><br />
<td width="69"><p align="center">7.82E-06</p></td><br />
<td width="69"><p align="center">2.13E-05</p></td><br />
<td width="69"><p align="center">8.50E-04</p></td><br />
<td width="69"><p align="center">7.97</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cobU</em></p></td><br />
<td width="69"><p align="center">4.26E-05</p></td><br />
<td width="69"><p align="center">1.22E-04</p></td><br />
<td width="69"><p align="center">1.79E-05</p></td><br />
<td width="69"><p align="center">1.95E-05</p></td><br />
<td width="69"><p align="center">9.90E-04</p></td><br />
<td width="69"><p align="center">2.85</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacA</em></p></td><br />
<td width="69"><p align="center">5.14E-03</p></td><br />
<td width="69"><p align="center">1.21E-03</p></td><br />
<td width="69"><p align="center">1.54E-03</p></td><br />
<td width="69"><p align="center">3.52E-04</p></td><br />
<td width="69"><p align="center">2.50E-03</p></td><br />
<td width="69"><p align="center">-4.24</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacZ</em></p></td><br />
<td width="69"><p align="center">2.10E-03</p></td><br />
<td width="69"><p align="center">5.14E-04</p></td><br />
<td width="69"><p align="center">3.77E-04</p></td><br />
<td width="69"><p align="center">1.34E-04</p></td><br />
<td width="69"><p align="center">2.20E-04</p></td><br />
<td width="69"><p align="center">-4.08</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacY</em></p></td><br />
<td width="69"><p align="center">1.62E-03</p></td><br />
<td width="69"><p align="center">4.08E-04</p></td><br />
<td width="69"><p align="center">2.53E-04</p></td><br />
<td width="69"><p align="center">7.95E-05</p></td><br />
<td width="69"><p align="center">9.80E-05</p></td><br />
<td width="69"><p align="center">-3.96</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ompF</em></p></td><br />
<td width="69"><p align="center">7.23E-03</p></td><br />
<td width="69"><p align="center">2.35E-03</p></td><br />
<td width="69"><p align="center">1.90E-03</p></td><br />
<td width="69"><p align="center">3.69E-04</p></td><br />
<td width="69"><p align="center">2.40E-03</p></td><br />
<td width="69"><p align="center">-3.07</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>gltD</em></p></td><br />
<td width="69"><p align="center">9.91E-04</p></td><br />
<td width="69"><p align="center">1.40E-04</p></td><br />
<td width="69"><p align="center">1.88E-04</p></td><br />
<td width="69"><p align="center">3.06E-05</p></td><br />
<td width="69"><p align="center">1.10E-04</p></td><br />
<td width="69"><p align="center">-7.1</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lpdA</em></p></td><br />
<td width="69"><p align="center">1.07E-03</p></td><br />
<td width="69"><p align="center">7.60E-04</p></td><br />
<td width="69"><p align="center">1.17E-04</p></td><br />
<td width="69"><p align="center">7.75E-05</p></td><br />
<td width="69"><p align="center">4.60E-03</p></td><br />
<td width="69"><p align="center">-1.41</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>rffT</em></p></td><br />
<td width="69"><p align="center">5.81E-06</p></td><br />
<td width="69"><p align="center">3.65E-05</p></td><br />
<td width="69"><p align="center">4.66E-06</p></td><br />
<td width="69"><p align="center">2.86E-05</p></td><br />
<td width="69"><p align="center">9.40E-04</p></td><br />
<td width="69"><p align="center">6.28</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ndh</em></p></td><br />
<td width="69"><p align="center">5.03E-05</p></td><br />
<td width="69"><p align="center">1.46E-04</p></td><br />
<td width="69"><p align="center">1.94E-05</p></td><br />
<td width="69"><p align="center">3.29E-05</p></td><br />
<td width="69"><p align="center">2.50E-03</p></td><br />
<td width="69"><p align="center">2.9</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cheR</em></p></td><br />
<td width="69"><p align="center">1.29E-04</p></td><br />
<td width="69"><p align="center">2.68E-05</p></td><br />
<td width="69"><p align="center">2.07E-04</p></td><br />
<td width="69"><p align="center">1.75E-05</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">-4.82</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>sodA</em></p></td><br />
<td width="69"><p align="center">3.80E-04</p></td><br />
<td width="69"><p align="center">9.74E-04</p></td><br />
<td width="69"><p align="center">1.06E-04</p></td><br />
<td width="69"><p align="center">6.26E-05</p></td><br />
<td width="69"><p align="center">7.00E-05</p></td><br />
<td width="69"><p align="center">2.57</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>sodB</em></p></td><br />
<td width="69"><p align="center">7.80E-04</p></td><br />
<td width="69"><p align="center">1.91E-03</p></td><br />
<td width="69"><p align="center">2.45E-04</p></td><br />
<td width="69"><p align="center">4.11E-04</p></td><br />
<td width="69"><p align="center">3.30E-03</p></td><br />
<td width="69"><p align="center">2.44</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cpdB</em></p></td><br />
<td width="69"><p align="center">1.92E-05</p></td><br />
<td width="69"><p align="center">7.56E-05</p></td><br />
<td width="69"><p align="center">1.24E-05</p></td><br />
<td width="69"><p align="center">1.40E-05</p></td><br />
<td width="69"><p align="center">9.50E-04</p></td><br />
<td width="69"><p align="center">3.94</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>guaA</em></p></td><br />
<td width="69"><p align="center">8.25E-04</p></td><br />
<td width="69"><p align="center">4.31E-04</p></td><br />
<td width="69"><p align="center">5.43E-05</p></td><br />
<td width="69"><p align="center">1.34E204</p></td><br />
<td width="69"><p align="center">1.60E203</p></td><br />
<td width="69"><p align="center">-1.91</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>yiaJ</em></p></td><br />
<td width="69"><p align="center">3.47E-05</p></td><br />
<td width="69"><p align="center">6.15E-04</p></td><br />
<td width="69"><p align="center">1.74E-05</p></td><br />
<td width="69"><p align="center">1.64E204</p></td><br />
<td width="69"><p align="center">4.10E204</p></td><br />
<td width="69"><p align="center">17.74</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>dsdX</em></p></td><br />
<td width="69"><p align="center">1.05E-05</p></td><br />
<td width="69"><p align="center">3.88E-05</p></td><br />
<td width="69"><p align="center">5.23E-06</p></td><br />
<td width="69"><p align="center">2.44E205</p></td><br />
<td width="69"><p align="center">1.70E203</p></td><br />
<td width="69"><p align="center">3.7</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppD</em></p></td><br />
<td width="69"><p align="center">2.32E-05</p></td><br />
<td width="69"><p align="center">8.02E-05</p></td><br />
<td width="69"><p align="center">1.81E-05</p></td><br />
<td width="69"><p align="center">1.66E205</p></td><br />
<td width="69"><p align="center">3.50E203</p></td><br />
<td width="69"><p align="center">3.46</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>glnL</em></p></td><br />
<td width="69"><p align="center">2.41E-04</p></td><br />
<td width="69"><p align="center">3.99E-05</p></td><br />
<td width="69"><p align="center">4.81E-05</p></td><br />
<td width="69"><p align="center">2.81E205</p></td><br />
<td width="69"><p align="center">3.60E204</p></td><br />
<td width="69"><p align="center">-6.04</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppA</em></p></td><br />
<td width="69"><p align="center">2.54E-03</p></td><br />
<td width="69"><p align="center">5.06E-03</p></td><br />
<td width="69"><p align="center">1.72E-04</p></td><br />
<td width="69"><p align="center">5.68E204</p></td><br />
<td width="69"><p align="center">1.40E204</p></td><br />
<td width="69"><p align="center">2</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppB</em></p></td><br />
<td width="69"><p align="center">1.06E-04</p></td><br />
<td width="69"><p align="center">3.57E-04</p></td><br />
<td width="69"><p align="center">3.06E-05</p></td><br />
<td width="69"><p align="center">6.22E205</p></td><br />
<td width="69"><p align="center">3.60E204</p></td><br />
<td width="69"><p align="center">3.35</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>proV</em></p></td><br />
<td width="69"><p align="center">2.50E-05</p></td><br />
<td width="69"><p align="center">5.30E-05</p></td><br />
<td width="69"><p align="center">7.34E-06</p></td><br />
<td width="69"><p align="center">9.57E206<strong></strong></p></td><br />
<td width="69"><p align="center">3.60E203</p></td><br />
<td width="69"><p align="center">2.12</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>rbsC</em></p></td><br />
<td width="69"><p align="center">4.20E-05</p></td><br />
<td width="69"><p align="center">1.12E-04</p></td><br />
<td width="69"><p align="center">1.47E-05</p></td><br />
<td width="69"><p align="center">2.70E205</p></td><br />
<td width="69"><p align="center">3.90E203</p></td><br />
<td width="69"><p align="center">2.67</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>hdeB</em></p></td><br />
<td width="69"><p align="center">1.09E-03</p></td><br />
<td width="69"><p align="center">5.51E-06</p></td><br />
<td width="69"><p align="center">1.80E-04</p></td><br />
<td width="69"><p align="center">3.47E206</p></td><br />
<td width="69"><p align="center">2.00E205</p></td><br />
<td width="69"><p align="center">-198.5</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>yefM</em></p></td><br />
<td width="69"><p align="center">4.63E-04</p></td><br />
<td width="69"><p align="center">8.12E-04</p></td><br />
<td width="69"><p align="center">5.02E-05</p></td><br />
<td width="69"><p align="center">6.07E205</p></td><br />
<td width="69"><p align="center">1.10E204</p></td><br />
<td width="69"><p align="center">1.75</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
</table><br />
<br />
<br />
<p align="justify">The comparing result means that whether the result of our software fits to the result of gene expression profile. After statistic, in these 30 genes, there are 21 genes whose result are same to gNAP's simulation, 70% of the total.<br />
</br>What’s more, it is easy to see that the result unfitted often from the same series of genes, such as ilv, cob, lac. After integrating those genes, the degree of fitness increased to 84%.<br />
</br>Therefore, we may draw the following conclusion that our software could simulate the impact of new gene to some extent.<br />
</p><br />
<br />
<h2>Reference</h2><br />
<p align="justify"><a href=”http://www.jbc.org/content/275/38/29672">Arfin S M, Long A D, Ito E T, et al. Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF INTEGRATION HOST FACTOR[J]. Journal of Biological Chemistry, 2000, 275(38): 29672-29684.</a></p><br />
<br />
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</div><br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/ExamplesTeam:USTC-Software/Project/Examples2013-09-26T15:31:32Z<p>USTCkun: </p>
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<h1>Examples</h1><br />
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<p align="justify">To prove our software’s reliability, we search for literatures to test our program. It is hard to find an appropriate literature which studies the effect of importing an exogenous gene into E.coli K-12. But actually, our software could also simulate the effect of changing endogenous gene by putting the same promoter and gene sequence in.</p><br />
<br />
<br />
<p align="justify">In this literature, Stuart and his team measured the gene expression profiles in otherwise isogenic integration host factor IHF+ and IHF- strains. And IHF is one of the genes in our genetic regulatory network(GRN). </p><br />
<br />
<p align="justify">By importing the IHF’s promoter and gene sequence, we used our software simulating the enhancement of IHF’s expression and compared the result with the gene expression profile in that literature.</p><br />
<br />
<p align="justify">There are 30 genes in that profile which are also in our GRN. Here is the list and Genes differentially expressed between E. coli K12 strains IH100 (IHF+) and IH105 (IHF-) with a p value less than 0.0005:</p><br />
<br />
<br />
<br />
<table width="1000px" border="0" cellspacing="0" cellpadding="0" style="border-top:2px #000000 solid;border-bottom:2px #000000 solid;"><br />
<tr><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Gene</p></td><br />
<td width="137" colspan="2" style="border-bottom:2px #000 solid;"><p align="center">Avg</p></td><br />
<td width="137" colspan="2" style="border-bottom:2px #000 solid;"><p align="center">S.D.</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">p value</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Fold</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Compare<br /><br />
Result</p></td><br />
</tr><br />
<tr><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH100</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH105</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH100</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH105</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>glnA</em></p></td><br />
<td width="69"><p align="center">2.91E-03</p></td><br />
<td width="69"><p align="center">9.39E-04</p></td><br />
<td width="69"><p align="center">6.80E-04</p></td><br />
<td width="69"><p align="center">1.33E-04</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">-3.1</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvA</em></p></td><br />
<td width="69"><p align="center">5.06E-04</p></td><br />
<td width="69"><p align="center">3.42E-04</p></td><br />
<td width="69"><p align="center">1.86E-05</p></td><br />
<td width="69"><p align="center">2.26E-05</p></td><br />
<td width="69"><p align="center">3.00E-05</p></td><br />
<td width="69"><p align="center">-1.48</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvE</em></p></td><br />
<td width="69"><p align="center">5.81E-04</p></td><br />
<td width="69"><p align="center">3.58E-04</p></td><br />
<td width="69"><p align="center">4.70E-05</p></td><br />
<td width="69"><p align="center">5.77E-05</p></td><br />
<td width="69"><p align="center">9.80E-04</p></td><br />
<td width="69"><p align="center">-1.62</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvG</em></p></td><br />
<td width="69"><p align="center">1.97E-04</p></td><br />
<td width="69"><p align="center">7.67E-05</p></td><br />
<td width="69"><p align="center">2.65E-05</p></td><br />
<td width="69"><p align="center">2.23E-05</p></td><br />
<td width="69"><p align="center">4.40E-04</p></td><br />
<td width="69"><p align="center">-2.57</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>leuA</em></p></td><br />
<td width="69"><p align="center">6.99E-04</p></td><br />
<td width="69"><p align="center">1.07E-03</p></td><br />
<td width="69"><p align="center">9.21E-05</p></td><br />
<td width="69"><p align="center">9.23E-05</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">1.53</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cobT</em></p></td><br />
<td width="69"><p align="center">1.00E-05</p></td><br />
<td width="69"><p align="center">7.97E-05</p></td><br />
<td width="69"><p align="center">7.82E-06</p></td><br />
<td width="69"><p align="center">2.13E-05</p></td><br />
<td width="69"><p align="center">8.50E-04</p></td><br />
<td width="69"><p align="center">7.97</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cobU</em></p></td><br />
<td width="69"><p align="center">4.26E-05</p></td><br />
<td width="69"><p align="center">1.22E-04</p></td><br />
<td width="69"><p align="center">1.79E-05</p></td><br />
<td width="69"><p align="center">1.95E-05</p></td><br />
<td width="69"><p align="center">9.90E-04</p></td><br />
<td width="69"><p align="center">2.85</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacA</em></p></td><br />
<td width="69"><p align="center">5.14E-03</p></td><br />
<td width="69"><p align="center">1.21E-03</p></td><br />
<td width="69"><p align="center">1.54E-03</p></td><br />
<td width="69"><p align="center">3.52E-04</p></td><br />
<td width="69"><p align="center">2.50E-03</p></td><br />
<td width="69"><p align="center">-4.24</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacZ</em></p></td><br />
<td width="69"><p align="center">2.10E-03</p></td><br />
<td width="69"><p align="center">5.14E-04</p></td><br />
<td width="69"><p align="center">3.77E-04</p></td><br />
<td width="69"><p align="center">1.34E-04</p></td><br />
<td width="69"><p align="center">2.20E-04</p></td><br />
<td width="69"><p align="center">-4.08</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacY</em></p></td><br />
<td width="69"><p align="center">1.62E-03</p></td><br />
<td width="69"><p align="center">4.08E-04</p></td><br />
<td width="69"><p align="center">2.53E-04</p></td><br />
<td width="69"><p align="center">7.95E-05</p></td><br />
<td width="69"><p align="center">9.80E-05</p></td><br />
<td width="69"><p align="center">-3.96</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ompF</em></p></td><br />
<td width="69"><p align="center">7.23E-03</p></td><br />
<td width="69"><p align="center">2.35E-03</p></td><br />
<td width="69"><p align="center">1.90E-03</p></td><br />
<td width="69"><p align="center">3.69E-04</p></td><br />
<td width="69"><p align="center">2.40E-03</p></td><br />
<td width="69"><p align="center">-3.07</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>gltD</em></p></td><br />
<td width="69"><p align="center">9.91E-04</p></td><br />
<td width="69"><p align="center">1.40E-04</p></td><br />
<td width="69"><p align="center">1.88E-04</p></td><br />
<td width="69"><p align="center">3.06E-05</p></td><br />
<td width="69"><p align="center">1.10E-04</p></td><br />
<td width="69"><p align="center">-7.1</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lpdA</em></p></td><br />
<td width="69"><p align="center">1.07E-03</p></td><br />
<td width="69"><p align="center">7.60E-04</p></td><br />
<td width="69"><p align="center">1.17E-04</p></td><br />
<td width="69"><p align="center">7.75E-05</p></td><br />
<td width="69"><p align="center">4.60E-03</p></td><br />
<td width="69"><p align="center">-1.41</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>rffT</em></p></td><br />
<td width="69"><p align="center">5.81E-06</p></td><br />
<td width="69"><p align="center">3.65E-05</p></td><br />
<td width="69"><p align="center">4.66E-06</p></td><br />
<td width="69"><p align="center">2.86E-05</p></td><br />
<td width="69"><p align="center">9.40E-04</p></td><br />
<td width="69"><p align="center">6.28</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ndh</em></p></td><br />
<td width="69"><p align="center">5.03E-05</p></td><br />
<td width="69"><p align="center">1.46E-04</p></td><br />
<td width="69"><p align="center">1.94E-05</p></td><br />
<td width="69"><p align="center">3.29E-05</p></td><br />
<td width="69"><p align="center">2.50E-03</p></td><br />
<td width="69"><p align="center">2.9</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cheR</em></p></td><br />
<td width="69"><p align="center">1.29E-04</p></td><br />
<td width="69"><p align="center">2.68E-05</p></td><br />
<td width="69"><p align="center">2.07E-04</p></td><br />
<td width="69"><p align="center">1.75E-05</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">-4.82</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>sodA</em></p></td><br />
<td width="69"><p align="center">3.80E-04</p></td><br />
<td width="69"><p align="center">9.74E-04</p></td><br />
<td width="69"><p align="center">1.06E-04</p></td><br />
<td width="69"><p align="center">6.26E-05</p></td><br />
<td width="69"><p align="center">7.00E-05</p></td><br />
<td width="69"><p align="center">2.57</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>sodB</em></p></td><br />
<td width="69"><p align="center">7.80E-04</p></td><br />
<td width="69"><p align="center">1.91E-03</p></td><br />
<td width="69"><p align="center">2.45E-04</p></td><br />
<td width="69"><p align="center">4.11E-04</p></td><br />
<td width="69"><p align="center">3.30E-03</p></td><br />
<td width="69"><p align="center">2.44</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cpdB</em></p></td><br />
<td width="69"><p align="center">1.92E-05</p></td><br />
<td width="69"><p align="center">7.56E-05</p></td><br />
<td width="69"><p align="center">1.24E-05</p></td><br />
<td width="69"><p align="center">1.40E-05</p></td><br />
<td width="69"><p align="center">9.50E-04</p></td><br />
<td width="69"><p align="center">3.94</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>guaA</em></p></td><br />
<td width="69"><p align="center">8.25E-04</p></td><br />
<td width="69"><p align="center">4.31E-04</p></td><br />
<td width="69"><p align="center">5.43E-05</p></td><br />
<td width="69"><p align="center">1.34E204</p></td><br />
<td width="69"><p align="center">1.60E203</p></td><br />
<td width="69"><p align="center">-1.91</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>yiaJ</em></p></td><br />
<td width="69"><p align="center">3.47E-05</p></td><br />
<td width="69"><p align="center">6.15E-04</p></td><br />
<td width="69"><p align="center">1.74E-05</p></td><br />
<td width="69"><p align="center">1.64E204</p></td><br />
<td width="69"><p align="center">4.10E204</p></td><br />
<td width="69"><p align="center">17.74</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>dsdX</em></p></td><br />
<td width="69"><p align="center">1.05E-05</p></td><br />
<td width="69"><p align="center">3.88E-05</p></td><br />
<td width="69"><p align="center">5.23E-06</p></td><br />
<td width="69"><p align="center">2.44E205</p></td><br />
<td width="69"><p align="center">1.70E203</p></td><br />
<td width="69"><p align="center">3.7</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppD</em></p></td><br />
<td width="69"><p align="center">2.32E-05</p></td><br />
<td width="69"><p align="center">8.02E-05</p></td><br />
<td width="69"><p align="center">1.81E-05</p></td><br />
<td width="69"><p align="center">1.66E205</p></td><br />
<td width="69"><p align="center">3.50E203</p></td><br />
<td width="69"><p align="center">3.46</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>glnL</em></p></td><br />
<td width="69"><p align="center">2.41E-04</p></td><br />
<td width="69"><p align="center">3.99E-05</p></td><br />
<td width="69"><p align="center">4.81E-05</p></td><br />
<td width="69"><p align="center">2.81E205</p></td><br />
<td width="69"><p align="center">3.60E204</p></td><br />
<td width="69"><p align="center">-6.04</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppA</em></p></td><br />
<td width="69"><p align="center">2.54E-03</p></td><br />
<td width="69"><p align="center">5.06E-03</p></td><br />
<td width="69"><p align="center">1.72E-04</p></td><br />
<td width="69"><p align="center">5.68E204</p></td><br />
<td width="69"><p align="center">1.40E204</p></td><br />
<td width="69"><p align="center">2</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppB</em></p></td><br />
<td width="69"><p align="center">1.06E-04</p></td><br />
<td width="69"><p align="center">3.57E-04</p></td><br />
<td width="69"><p align="center">3.06E-05</p></td><br />
<td width="69"><p align="center">6.22E205</p></td><br />
<td width="69"><p align="center">3.60E204</p></td><br />
<td width="69"><p align="center">3.35</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>proV</em></p></td><br />
<td width="69"><p align="center">2.50E-05</p></td><br />
<td width="69"><p align="center">5.30E-05</p></td><br />
<td width="69"><p align="center">7.34E-06</p></td><br />
<td width="69"><p align="center">9.57E206<strong></strong></p></td><br />
<td width="69"><p align="center">3.60E203</p></td><br />
<td width="69"><p align="center">2.12</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>rbsC</em></p></td><br />
<td width="69"><p align="center">4.20E-05</p></td><br />
<td width="69"><p align="center">1.12E-04</p></td><br />
<td width="69"><p align="center">1.47E-05</p></td><br />
<td width="69"><p align="center">2.70E205</p></td><br />
<td width="69"><p align="center">3.90E203</p></td><br />
<td width="69"><p align="center">2.67</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>hdeB</em></p></td><br />
<td width="69"><p align="center">1.09E-03</p></td><br />
<td width="69"><p align="center">5.51E-06</p></td><br />
<td width="69"><p align="center">1.80E-04</p></td><br />
<td width="69"><p align="center">3.47E206</p></td><br />
<td width="69"><p align="center">2.00E205</p></td><br />
<td width="69"><p align="center">-198.5</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>yefM</em></p></td><br />
<td width="69"><p align="center">4.63E-04</p></td><br />
<td width="69"><p align="center">8.12E-04</p></td><br />
<td width="69"><p align="center">5.02E-05</p></td><br />
<td width="69"><p align="center">6.07E205</p></td><br />
<td width="69"><p align="center">1.10E204</p></td><br />
<td width="69"><p align="center">1.75</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
</table><br />
<br />
<br />
<p align="justify">The comparing result means that whether the result of our software fits to the result of gene expression profile. After statistic, in these 30 genes, there are 21 genes whose result are same to gNAP's simulation, 70% of the total.<br />
</br>What’s more, it is easy to see that the result unfitted often from the same series of genes, such as ilv, cob, lac. After integrating those genes, the degree of fitness increased to 84%.<br />
</br>Therefore, we may draw the following conclusion that our software could simulate the impact of new gene to some extent.<br />
</p><br />
<br />
<h2>Reference</h2><br />
<p align="justify"><a href=”http://www.jbc.org/content/275/38/29672">Arfin S M, Long A D, Ito E T, et al. Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF INTEGRATION HOST FACTOR[J]. Journal of Biological Chemistry, 2000, 275(38): 29672-29684.</a></p><br />
<br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/ExamplesTeam:USTC-Software/Project/Examples2013-09-26T15:28:35Z<p>USTCkun: </p>
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<h1>Examples</h1><br />
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<p align="justify">To prove our software’s reliability, we search for lots of literatures. It is hard to find an appropriate literature which studies the effect of importing an exogenous gene into E.coli K-12. But actually, our software could also simulate the effect of changing endogenous gene by putting the same promoter and gene sequence in.</p><br />
<br />
<p align="justify">So, we found this literature:</p><br />
<br />
<br />
<p align="justify"><a id="content" href="http://www.jbc.org/content/275/38/29672">Global Gene Expression Profiling in Escherichia coliK12 </br>THE EFFECTS OF INTEGRATION HOST FACTOR</a></p><br />
<br />
<p align="justify">In this literature, Stuart and his team measured the gene expression profiles in otherwise isogenic integration host factor IHF+ and IHF- strains. And IHF is one of the genes in our genetic regulatory network(GRN). </p><br />
<br />
<p align="justify">By importing the IHF’s promoter and gene sequence, we used our software simulating the enhancement of IHF’s expression and compared the result with the gene expression profile in that literature.</p><br />
<br />
<p align="justify">There are 30 genes in that profile which are also in our GRN. Here is the list and Genes differentially expressed between E. coli K12 strains IH100 (IHF+) and IH105 (IHF-) with a p value less than 0.0005:</p><br />
<br />
<br />
<br />
<table width="1000px" border="0" cellspacing="0" cellpadding="0" style="border-top:2px #000000 solid;border-bottom:2px #000000 solid;"><br />
<tr><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Gene</p></td><br />
<td width="137" colspan="2" style="border-bottom:2px #000 solid;"><p align="center">Avg</p></td><br />
<td width="137" colspan="2" style="border-bottom:2px #000 solid;"><p align="center">S.D.</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">p value</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Fold</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Compare<br /><br />
Result</p></td><br />
</tr><br />
<tr><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH100</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH105</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH100</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH105</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>glnA</em></p></td><br />
<td width="69"><p align="center">2.91E-03</p></td><br />
<td width="69"><p align="center">9.39E-04</p></td><br />
<td width="69"><p align="center">6.80E-04</p></td><br />
<td width="69"><p align="center">1.33E-04</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">-3.1</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvA</em></p></td><br />
<td width="69"><p align="center">5.06E-04</p></td><br />
<td width="69"><p align="center">3.42E-04</p></td><br />
<td width="69"><p align="center">1.86E-05</p></td><br />
<td width="69"><p align="center">2.26E-05</p></td><br />
<td width="69"><p align="center">3.00E-05</p></td><br />
<td width="69"><p align="center">-1.48</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvE</em></p></td><br />
<td width="69"><p align="center">5.81E-04</p></td><br />
<td width="69"><p align="center">3.58E-04</p></td><br />
<td width="69"><p align="center">4.70E-05</p></td><br />
<td width="69"><p align="center">5.77E-05</p></td><br />
<td width="69"><p align="center">9.80E-04</p></td><br />
<td width="69"><p align="center">-1.62</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvG</em></p></td><br />
<td width="69"><p align="center">1.97E-04</p></td><br />
<td width="69"><p align="center">7.67E-05</p></td><br />
<td width="69"><p align="center">2.65E-05</p></td><br />
<td width="69"><p align="center">2.23E-05</p></td><br />
<td width="69"><p align="center">4.40E-04</p></td><br />
<td width="69"><p align="center">-2.57</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>leuA</em></p></td><br />
<td width="69"><p align="center">6.99E-04</p></td><br />
<td width="69"><p align="center">1.07E-03</p></td><br />
<td width="69"><p align="center">9.21E-05</p></td><br />
<td width="69"><p align="center">9.23E-05</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">1.53</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cobT</em></p></td><br />
<td width="69"><p align="center">1.00E-05</p></td><br />
<td width="69"><p align="center">7.97E-05</p></td><br />
<td width="69"><p align="center">7.82E-06</p></td><br />
<td width="69"><p align="center">2.13E-05</p></td><br />
<td width="69"><p align="center">8.50E-04</p></td><br />
<td width="69"><p align="center">7.97</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cobU</em></p></td><br />
<td width="69"><p align="center">4.26E-05</p></td><br />
<td width="69"><p align="center">1.22E-04</p></td><br />
<td width="69"><p align="center">1.79E-05</p></td><br />
<td width="69"><p align="center">1.95E-05</p></td><br />
<td width="69"><p align="center">9.90E-04</p></td><br />
<td width="69"><p align="center">2.85</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacA</em></p></td><br />
<td width="69"><p align="center">5.14E-03</p></td><br />
<td width="69"><p align="center">1.21E-03</p></td><br />
<td width="69"><p align="center">1.54E-03</p></td><br />
<td width="69"><p align="center">3.52E-04</p></td><br />
<td width="69"><p align="center">2.50E-03</p></td><br />
<td width="69"><p align="center">-4.24</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacZ</em></p></td><br />
<td width="69"><p align="center">2.10E-03</p></td><br />
<td width="69"><p align="center">5.14E-04</p></td><br />
<td width="69"><p align="center">3.77E-04</p></td><br />
<td width="69"><p align="center">1.34E-04</p></td><br />
<td width="69"><p align="center">2.20E-04</p></td><br />
<td width="69"><p align="center">-4.08</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacY</em></p></td><br />
<td width="69"><p align="center">1.62E-03</p></td><br />
<td width="69"><p align="center">4.08E-04</p></td><br />
<td width="69"><p align="center">2.53E-04</p></td><br />
<td width="69"><p align="center">7.95E-05</p></td><br />
<td width="69"><p align="center">9.80E-05</p></td><br />
<td width="69"><p align="center">-3.96</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ompF</em></p></td><br />
<td width="69"><p align="center">7.23E-03</p></td><br />
<td width="69"><p align="center">2.35E-03</p></td><br />
<td width="69"><p align="center">1.90E-03</p></td><br />
<td width="69"><p align="center">3.69E-04</p></td><br />
<td width="69"><p align="center">2.40E-03</p></td><br />
<td width="69"><p align="center">-3.07</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>gltD</em></p></td><br />
<td width="69"><p align="center">9.91E-04</p></td><br />
<td width="69"><p align="center">1.40E-04</p></td><br />
<td width="69"><p align="center">1.88E-04</p></td><br />
<td width="69"><p align="center">3.06E-05</p></td><br />
<td width="69"><p align="center">1.10E-04</p></td><br />
<td width="69"><p align="center">-7.1</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lpdA</em></p></td><br />
<td width="69"><p align="center">1.07E-03</p></td><br />
<td width="69"><p align="center">7.60E-04</p></td><br />
<td width="69"><p align="center">1.17E-04</p></td><br />
<td width="69"><p align="center">7.75E-05</p></td><br />
<td width="69"><p align="center">4.60E-03</p></td><br />
<td width="69"><p align="center">-1.41</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>rffT</em></p></td><br />
<td width="69"><p align="center">5.81E-06</p></td><br />
<td width="69"><p align="center">3.65E-05</p></td><br />
<td width="69"><p align="center">4.66E-06</p></td><br />
<td width="69"><p align="center">2.86E-05</p></td><br />
<td width="69"><p align="center">9.40E-04</p></td><br />
<td width="69"><p align="center">6.28</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ndh</em></p></td><br />
<td width="69"><p align="center">5.03E-05</p></td><br />
<td width="69"><p align="center">1.46E-04</p></td><br />
<td width="69"><p align="center">1.94E-05</p></td><br />
<td width="69"><p align="center">3.29E-05</p></td><br />
<td width="69"><p align="center">2.50E-03</p></td><br />
<td width="69"><p align="center">2.9</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cheR</em></p></td><br />
<td width="69"><p align="center">1.29E-04</p></td><br />
<td width="69"><p align="center">2.68E-05</p></td><br />
<td width="69"><p align="center">2.07E-04</p></td><br />
<td width="69"><p align="center">1.75E-05</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">-4.82</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>sodA</em></p></td><br />
<td width="69"><p align="center">3.80E-04</p></td><br />
<td width="69"><p align="center">9.74E-04</p></td><br />
<td width="69"><p align="center">1.06E-04</p></td><br />
<td width="69"><p align="center">6.26E-05</p></td><br />
<td width="69"><p align="center">7.00E-05</p></td><br />
<td width="69"><p align="center">2.57</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>sodB</em></p></td><br />
<td width="69"><p align="center">7.80E-04</p></td><br />
<td width="69"><p align="center">1.91E-03</p></td><br />
<td width="69"><p align="center">2.45E-04</p></td><br />
<td width="69"><p align="center">4.11E-04</p></td><br />
<td width="69"><p align="center">3.30E-03</p></td><br />
<td width="69"><p align="center">2.44</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cpdB</em></p></td><br />
<td width="69"><p align="center">1.92E-05</p></td><br />
<td width="69"><p align="center">7.56E-05</p></td><br />
<td width="69"><p align="center">1.24E-05</p></td><br />
<td width="69"><p align="center">1.40E-05</p></td><br />
<td width="69"><p align="center">9.50E-04</p></td><br />
<td width="69"><p align="center">3.94</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>guaA</em></p></td><br />
<td width="69"><p align="center">8.25E-04</p></td><br />
<td width="69"><p align="center">4.31E-04</p></td><br />
<td width="69"><p align="center">5.43E-05</p></td><br />
<td width="69"><p align="center">1.34E204</p></td><br />
<td width="69"><p align="center">1.60E203</p></td><br />
<td width="69"><p align="center">-1.91</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>yiaJ</em></p></td><br />
<td width="69"><p align="center">3.47E-05</p></td><br />
<td width="69"><p align="center">6.15E-04</p></td><br />
<td width="69"><p align="center">1.74E-05</p></td><br />
<td width="69"><p align="center">1.64E204</p></td><br />
<td width="69"><p align="center">4.10E204</p></td><br />
<td width="69"><p align="center">17.74</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>dsdX</em></p></td><br />
<td width="69"><p align="center">1.05E-05</p></td><br />
<td width="69"><p align="center">3.88E-05</p></td><br />
<td width="69"><p align="center">5.23E-06</p></td><br />
<td width="69"><p align="center">2.44E205</p></td><br />
<td width="69"><p align="center">1.70E203</p></td><br />
<td width="69"><p align="center">3.7</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppD</em></p></td><br />
<td width="69"><p align="center">2.32E-05</p></td><br />
<td width="69"><p align="center">8.02E-05</p></td><br />
<td width="69"><p align="center">1.81E-05</p></td><br />
<td width="69"><p align="center">1.66E205</p></td><br />
<td width="69"><p align="center">3.50E203</p></td><br />
<td width="69"><p align="center">3.46</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>glnL</em></p></td><br />
<td width="69"><p align="center">2.41E-04</p></td><br />
<td width="69"><p align="center">3.99E-05</p></td><br />
<td width="69"><p align="center">4.81E-05</p></td><br />
<td width="69"><p align="center">2.81E205</p></td><br />
<td width="69"><p align="center">3.60E204</p></td><br />
<td width="69"><p align="center">-6.04</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppA</em></p></td><br />
<td width="69"><p align="center">2.54E-03</p></td><br />
<td width="69"><p align="center">5.06E-03</p></td><br />
<td width="69"><p align="center">1.72E-04</p></td><br />
<td width="69"><p align="center">5.68E204</p></td><br />
<td width="69"><p align="center">1.40E204</p></td><br />
<td width="69"><p align="center">2</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppB</em></p></td><br />
<td width="69"><p align="center">1.06E-04</p></td><br />
<td width="69"><p align="center">3.57E-04</p></td><br />
<td width="69"><p align="center">3.06E-05</p></td><br />
<td width="69"><p align="center">6.22E205</p></td><br />
<td width="69"><p align="center">3.60E204</p></td><br />
<td width="69"><p align="center">3.35</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>proV</em></p></td><br />
<td width="69"><p align="center">2.50E-05</p></td><br />
<td width="69"><p align="center">5.30E-05</p></td><br />
<td width="69"><p align="center">7.34E-06</p></td><br />
<td width="69"><p align="center">9.57E206<strong></strong></p></td><br />
<td width="69"><p align="center">3.60E203</p></td><br />
<td width="69"><p align="center">2.12</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>rbsC</em></p></td><br />
<td width="69"><p align="center">4.20E-05</p></td><br />
<td width="69"><p align="center">1.12E-04</p></td><br />
<td width="69"><p align="center">1.47E-05</p></td><br />
<td width="69"><p align="center">2.70E205</p></td><br />
<td width="69"><p align="center">3.90E203</p></td><br />
<td width="69"><p align="center">2.67</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>hdeB</em></p></td><br />
<td width="69"><p align="center">1.09E-03</p></td><br />
<td width="69"><p align="center">5.51E-06</p></td><br />
<td width="69"><p align="center">1.80E-04</p></td><br />
<td width="69"><p align="center">3.47E206</p></td><br />
<td width="69"><p align="center">2.00E205</p></td><br />
<td width="69"><p align="center">-198.5</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>yefM</em></p></td><br />
<td width="69"><p align="center">4.63E-04</p></td><br />
<td width="69"><p align="center">8.12E-04</p></td><br />
<td width="69"><p align="center">5.02E-05</p></td><br />
<td width="69"><p align="center">6.07E205</p></td><br />
<td width="69"><p align="center">1.10E204</p></td><br />
<td width="69"><p align="center">1.75</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
</table><br />
<br />
<br />
<p align="justify">The comparing result means that whether the result of our software fits to the result of gene expression profile. After statistic, in these 30 genes, there are 21 genes whose result are same to gNAP's simulation, 70% of the total.<br />
</br>What’s more, it is easy to see that the result unfitted often from the same series of genes, such as ilv, cob, lac. After integrating those genes, the degree of fitness increased to 84%.<br />
</br>Therefore, we may draw the following conclusion that our software could simulate the impact of new gene to some extent.<br />
</p><br />
<br />
<h2>Reference</h2><br />
<p align="justify">Arfin S M, Long A D, Ito E T, et al. Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF INTEGRATION HOST FACTOR[J]. Journal of Biological Chemistry, 2000, 275(38): 29672-29684.</p><br />
<br />
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</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/ExamplesTeam:USTC-Software/Project/Examples2013-09-26T15:28:05Z<p>USTCkun: </p>
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<h1>Examples</h1><br />
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<p align="justify">To prove our software’s reliability, we search for lots of literatures. It is hard to find an appropriate literature which studies the effect of importing an exogenous gene into E.coli K-12. But actually, our software could also simulate the effect of changing endogenous gene by putting the same promoter and gene sequence in.</p><br />
<br />
<p align="justify">So, we found this literature:</p><br />
<br />
<br />
<p align="justify"><a id="content" href="http://www.jbc.org/content/275/38/29672">Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF INTEGRATION HOST FACTOR</a></p><br />
<br />
<p align="justify">In this literature, Stuart and his team measured the gene expression profiles in otherwise isogenic integration host factor IHF+ and IHF- strains. And IHF is one of the genes in our genetic regulatory network(GRN). </p><br />
<br />
<p align="justify">By importing the IHF’s promoter and gene sequence, we used our software simulating the enhancement of IHF’s expression and compared the result with the gene expression profile in that literature.</p><br />
<br />
<p align="justify">There are 30 genes in that profile which are also in our GRN. Here is the list and Genes differentially expressed between E. coli K12 strains IH100 (IHF+) and IH105 (IHF-) with a p value less than 0.0005:</p><br />
<br />
<br />
<br />
<table width="1000px" border="0" cellspacing="0" cellpadding="0" style="border-top:2px #000000 solid;border-bottom:2px #000000 solid;"><br />
<tr><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Gene</p></td><br />
<td width="137" colspan="2" style="border-bottom:2px #000 solid;"><p align="center">Avg</p></td><br />
<td width="137" colspan="2" style="border-bottom:2px #000 solid;"><p align="center">S.D.</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">p value</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Fold</p></td><br />
<td width="69" rowspan="2" style="border-bottom:2px #000 solid;"><p align="center">Compare<br /><br />
Result</p></td><br />
</tr><br />
<tr><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH100</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH105</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH100</p></td><br />
<td width="69" style="border-bottom:2px #000 solid;"><p align="center">IH105</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>glnA</em></p></td><br />
<td width="69"><p align="center">2.91E-03</p></td><br />
<td width="69"><p align="center">9.39E-04</p></td><br />
<td width="69"><p align="center">6.80E-04</p></td><br />
<td width="69"><p align="center">1.33E-04</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">-3.1</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvA</em></p></td><br />
<td width="69"><p align="center">5.06E-04</p></td><br />
<td width="69"><p align="center">3.42E-04</p></td><br />
<td width="69"><p align="center">1.86E-05</p></td><br />
<td width="69"><p align="center">2.26E-05</p></td><br />
<td width="69"><p align="center">3.00E-05</p></td><br />
<td width="69"><p align="center">-1.48</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvE</em></p></td><br />
<td width="69"><p align="center">5.81E-04</p></td><br />
<td width="69"><p align="center">3.58E-04</p></td><br />
<td width="69"><p align="center">4.70E-05</p></td><br />
<td width="69"><p align="center">5.77E-05</p></td><br />
<td width="69"><p align="center">9.80E-04</p></td><br />
<td width="69"><p align="center">-1.62</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ilvG</em></p></td><br />
<td width="69"><p align="center">1.97E-04</p></td><br />
<td width="69"><p align="center">7.67E-05</p></td><br />
<td width="69"><p align="center">2.65E-05</p></td><br />
<td width="69"><p align="center">2.23E-05</p></td><br />
<td width="69"><p align="center">4.40E-04</p></td><br />
<td width="69"><p align="center">-2.57</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>leuA</em></p></td><br />
<td width="69"><p align="center">6.99E-04</p></td><br />
<td width="69"><p align="center">1.07E-03</p></td><br />
<td width="69"><p align="center">9.21E-05</p></td><br />
<td width="69"><p align="center">9.23E-05</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">1.53</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cobT</em></p></td><br />
<td width="69"><p align="center">1.00E-05</p></td><br />
<td width="69"><p align="center">7.97E-05</p></td><br />
<td width="69"><p align="center">7.82E-06</p></td><br />
<td width="69"><p align="center">2.13E-05</p></td><br />
<td width="69"><p align="center">8.50E-04</p></td><br />
<td width="69"><p align="center">7.97</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cobU</em></p></td><br />
<td width="69"><p align="center">4.26E-05</p></td><br />
<td width="69"><p align="center">1.22E-04</p></td><br />
<td width="69"><p align="center">1.79E-05</p></td><br />
<td width="69"><p align="center">1.95E-05</p></td><br />
<td width="69"><p align="center">9.90E-04</p></td><br />
<td width="69"><p align="center">2.85</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacA</em></p></td><br />
<td width="69"><p align="center">5.14E-03</p></td><br />
<td width="69"><p align="center">1.21E-03</p></td><br />
<td width="69"><p align="center">1.54E-03</p></td><br />
<td width="69"><p align="center">3.52E-04</p></td><br />
<td width="69"><p align="center">2.50E-03</p></td><br />
<td width="69"><p align="center">-4.24</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacZ</em></p></td><br />
<td width="69"><p align="center">2.10E-03</p></td><br />
<td width="69"><p align="center">5.14E-04</p></td><br />
<td width="69"><p align="center">3.77E-04</p></td><br />
<td width="69"><p align="center">1.34E-04</p></td><br />
<td width="69"><p align="center">2.20E-04</p></td><br />
<td width="69"><p align="center">-4.08</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lacY</em></p></td><br />
<td width="69"><p align="center">1.62E-03</p></td><br />
<td width="69"><p align="center">4.08E-04</p></td><br />
<td width="69"><p align="center">2.53E-04</p></td><br />
<td width="69"><p align="center">7.95E-05</p></td><br />
<td width="69"><p align="center">9.80E-05</p></td><br />
<td width="69"><p align="center">-3.96</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ompF</em></p></td><br />
<td width="69"><p align="center">7.23E-03</p></td><br />
<td width="69"><p align="center">2.35E-03</p></td><br />
<td width="69"><p align="center">1.90E-03</p></td><br />
<td width="69"><p align="center">3.69E-04</p></td><br />
<td width="69"><p align="center">2.40E-03</p></td><br />
<td width="69"><p align="center">-3.07</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>gltD</em></p></td><br />
<td width="69"><p align="center">9.91E-04</p></td><br />
<td width="69"><p align="center">1.40E-04</p></td><br />
<td width="69"><p align="center">1.88E-04</p></td><br />
<td width="69"><p align="center">3.06E-05</p></td><br />
<td width="69"><p align="center">1.10E-04</p></td><br />
<td width="69"><p align="center">-7.1</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>lpdA</em></p></td><br />
<td width="69"><p align="center">1.07E-03</p></td><br />
<td width="69"><p align="center">7.60E-04</p></td><br />
<td width="69"><p align="center">1.17E-04</p></td><br />
<td width="69"><p align="center">7.75E-05</p></td><br />
<td width="69"><p align="center">4.60E-03</p></td><br />
<td width="69"><p align="center">-1.41</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>rffT</em></p></td><br />
<td width="69"><p align="center">5.81E-06</p></td><br />
<td width="69"><p align="center">3.65E-05</p></td><br />
<td width="69"><p align="center">4.66E-06</p></td><br />
<td width="69"><p align="center">2.86E-05</p></td><br />
<td width="69"><p align="center">9.40E-04</p></td><br />
<td width="69"><p align="center">6.28</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>ndh</em></p></td><br />
<td width="69"><p align="center">5.03E-05</p></td><br />
<td width="69"><p align="center">1.46E-04</p></td><br />
<td width="69"><p align="center">1.94E-05</p></td><br />
<td width="69"><p align="center">3.29E-05</p></td><br />
<td width="69"><p align="center">2.50E-03</p></td><br />
<td width="69"><p align="center">2.9</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cheR</em></p></td><br />
<td width="69"><p align="center">1.29E-04</p></td><br />
<td width="69"><p align="center">2.68E-05</p></td><br />
<td width="69"><p align="center">2.07E-04</p></td><br />
<td width="69"><p align="center">1.75E-05</p></td><br />
<td width="69"><p align="center">1.30E-03</p></td><br />
<td width="69"><p align="center">-4.82</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>sodA</em></p></td><br />
<td width="69"><p align="center">3.80E-04</p></td><br />
<td width="69"><p align="center">9.74E-04</p></td><br />
<td width="69"><p align="center">1.06E-04</p></td><br />
<td width="69"><p align="center">6.26E-05</p></td><br />
<td width="69"><p align="center">7.00E-05</p></td><br />
<td width="69"><p align="center">2.57</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>sodB</em></p></td><br />
<td width="69"><p align="center">7.80E-04</p></td><br />
<td width="69"><p align="center">1.91E-03</p></td><br />
<td width="69"><p align="center">2.45E-04</p></td><br />
<td width="69"><p align="center">4.11E-04</p></td><br />
<td width="69"><p align="center">3.30E-03</p></td><br />
<td width="69"><p align="center">2.44</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>cpdB</em></p></td><br />
<td width="69"><p align="center">1.92E-05</p></td><br />
<td width="69"><p align="center">7.56E-05</p></td><br />
<td width="69"><p align="center">1.24E-05</p></td><br />
<td width="69"><p align="center">1.40E-05</p></td><br />
<td width="69"><p align="center">9.50E-04</p></td><br />
<td width="69"><p align="center">3.94</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>guaA</em></p></td><br />
<td width="69"><p align="center">8.25E-04</p></td><br />
<td width="69"><p align="center">4.31E-04</p></td><br />
<td width="69"><p align="center">5.43E-05</p></td><br />
<td width="69"><p align="center">1.34E204</p></td><br />
<td width="69"><p align="center">1.60E203</p></td><br />
<td width="69"><p align="center">-1.91</p></td><br />
<td width="69"><p align="center">unfit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>yiaJ</em></p></td><br />
<td width="69"><p align="center">3.47E-05</p></td><br />
<td width="69"><p align="center">6.15E-04</p></td><br />
<td width="69"><p align="center">1.74E-05</p></td><br />
<td width="69"><p align="center">1.64E204</p></td><br />
<td width="69"><p align="center">4.10E204</p></td><br />
<td width="69"><p align="center">17.74</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>dsdX</em></p></td><br />
<td width="69"><p align="center">1.05E-05</p></td><br />
<td width="69"><p align="center">3.88E-05</p></td><br />
<td width="69"><p align="center">5.23E-06</p></td><br />
<td width="69"><p align="center">2.44E205</p></td><br />
<td width="69"><p align="center">1.70E203</p></td><br />
<td width="69"><p align="center">3.7</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppD</em></p></td><br />
<td width="69"><p align="center">2.32E-05</p></td><br />
<td width="69"><p align="center">8.02E-05</p></td><br />
<td width="69"><p align="center">1.81E-05</p></td><br />
<td width="69"><p align="center">1.66E205</p></td><br />
<td width="69"><p align="center">3.50E203</p></td><br />
<td width="69"><p align="center">3.46</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>glnL</em></p></td><br />
<td width="69"><p align="center">2.41E-04</p></td><br />
<td width="69"><p align="center">3.99E-05</p></td><br />
<td width="69"><p align="center">4.81E-05</p></td><br />
<td width="69"><p align="center">2.81E205</p></td><br />
<td width="69"><p align="center">3.60E204</p></td><br />
<td width="69"><p align="center">-6.04</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppA</em></p></td><br />
<td width="69"><p align="center">2.54E-03</p></td><br />
<td width="69"><p align="center">5.06E-03</p></td><br />
<td width="69"><p align="center">1.72E-04</p></td><br />
<td width="69"><p align="center">5.68E204</p></td><br />
<td width="69"><p align="center">1.40E204</p></td><br />
<td width="69"><p align="center">2</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>oppB</em></p></td><br />
<td width="69"><p align="center">1.06E-04</p></td><br />
<td width="69"><p align="center">3.57E-04</p></td><br />
<td width="69"><p align="center">3.06E-05</p></td><br />
<td width="69"><p align="center">6.22E205</p></td><br />
<td width="69"><p align="center">3.60E204</p></td><br />
<td width="69"><p align="center">3.35</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>proV</em></p></td><br />
<td width="69"><p align="center">2.50E-05</p></td><br />
<td width="69"><p align="center">5.30E-05</p></td><br />
<td width="69"><p align="center">7.34E-06</p></td><br />
<td width="69"><p align="center">9.57E206<strong></strong></p></td><br />
<td width="69"><p align="center">3.60E203</p></td><br />
<td width="69"><p align="center">2.12</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>rbsC</em></p></td><br />
<td width="69"><p align="center">4.20E-05</p></td><br />
<td width="69"><p align="center">1.12E-04</p></td><br />
<td width="69"><p align="center">1.47E-05</p></td><br />
<td width="69"><p align="center">2.70E205</p></td><br />
<td width="69"><p align="center">3.90E203</p></td><br />
<td width="69"><p align="center">2.67</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>hdeB</em></p></td><br />
<td width="69"><p align="center">1.09E-03</p></td><br />
<td width="69"><p align="center">5.51E-06</p></td><br />
<td width="69"><p align="center">1.80E-04</p></td><br />
<td width="69"><p align="center">3.47E206</p></td><br />
<td width="69"><p align="center">2.00E205</p></td><br />
<td width="69"><p align="center">-198.5</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
<tr><br />
<td width="69"><p align="center"><em>yefM</em></p></td><br />
<td width="69"><p align="center">4.63E-04</p></td><br />
<td width="69"><p align="center">8.12E-04</p></td><br />
<td width="69"><p align="center">5.02E-05</p></td><br />
<td width="69"><p align="center">6.07E205</p></td><br />
<td width="69"><p align="center">1.10E204</p></td><br />
<td width="69"><p align="center">1.75</p></td><br />
<td width="69"><p align="center">fit</p></td><br />
</tr><br />
</table><br />
<br />
<br />
<p align="justify">The comparing result means that whether the result of our software fits to the result of gene expression profile. After statistic, in these 30 genes, there are 21 genes whose result are same to gNAP's simulation, 70% of the total.<br />
</br>What’s more, it is easy to see that the result unfitted often from the same series of genes, such as ilv, cob, lac. After integrating those genes, the degree of fitness increased to 84%.<br />
</br>Therefore, we may draw the following conclusion that our software could simulate the impact of new gene to some extent.<br />
</p><br />
<br />
<h2>Reference</h2><br />
<p align="justify">Arfin S M, Long A D, Ito E T, et al. Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF INTEGRATION HOST FACTOR[J]. Journal of Biological Chemistry, 2000, 275(38): 29672-29684.</p><br />
<br />
<br />
</div><br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-SoftwareTeam:USTC-Software2013-09-26T10:33:13Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<br />
<title>Home</title><br />
<br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/css/camera?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/css/thanks?action=raw&ctype=text/css" type="text/css"/><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<br />
<script src="https://2013.igem.org/Team:USTC-Software/js/camera?action=raw&ctype=text/javascript" type="text/javascript" charset="utf-8"></script><br />
<br />
<br />
<br />
</head><br />
<br />
<body><br />
<br />
<br />
<div id="camera"><br />
<div class="camera_wrap camera_charcoal_skin" id="camera_wrap_1" align="center"><br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/7/7c/USTC_Software_temp1.jpg<br />
" data-src="https://static.igem.org/mediawiki/2013/8/85/USTC_Software_Page1.png<br />
"><br />
<div class="camera_caption fadeFromBottom"><em>Take a gNAP before wearing your gloves!<br/>Genetic Network Analyze and Predict</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/9/99/USTC_Software_temp2.jpg<br />
" data-src="https://static.igem.org/mediawiki/2013/1/15/2.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>The sketch and final GUI of gNAP!</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/5/5b/USTC_Software_temp3.jpg" data-src="https://static.igem.org/mediawiki/igem.org/0/0d/USTC_Software_three.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We compare the result of our software with gene expression profile in literature.</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/a/ae/USTC_Software_temp4.jpg" data-src="https://static.igem.org/mediawiki/2013/c/cb/USTC_Software_4.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We are USTC-Software!</em></div><br />
</div><br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
<div id="green"><br />
<div class="right"><br />
<h2>Overall</h2><br />
<p align="justify">Genetic Network Analyze and Predict (gNAP) is a software which can model and analyze the change of new GRN constructing after exogenous gene’s import. What’s more, gNAP’s inverse prediction based on overall GRN gives some advise to experimenters before choosing the imported gene meeting their purpose.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Overall" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/a/a6/USTC_Software_Overall.png"/><br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
<div id="cyan"><br />
<br />
<div class="right"><br />
<h2><span class="head">Software</span></h2><br />
<p align="justify"><span style="font-family:Arial, Helvetica, sans-serif;">gNAP’s source code has been written in C++ language and the visualization parts have been written in JAVA language. By using Qt Creator, we designed our GUI which was in plain style. All of them can be compiled across platforms. There are four major parts in gNAP called Start, Monitor, Result and Display.<br/></span></p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Software" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6e/USTC_Software_Software.png" /><br />
</div><br />
<br />
</div><br />
<br />
<br />
<br />
<div id="blue"><br />
<br />
<div class="right"><br />
<h2>Methodologies</h2><br />
<p align="justify">In order to simulate the GRN’s working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</p><br />
<br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Method" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d7/USTC_Software_Method.png" /><br />
</div><br />
<br />
</div><br />
<br />
<br />
<br />
<br />
<div id="purple"><br />
<br />
<div class="right"><br />
<h2><span class="head">Human Practice</span></h2><br />
<p align="justify">This year, our human practice were held on our campus. We would like to say "hi" for iGEM to every teacher and student in USTC and also bring their "Hello" to iGEM. What’s more, We hope that there will be more people who are interested in synthetic biology and programming joining in iGEM through our human practice.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/HP" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c0/USTC_Software_Humanpractice.png"/><br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
<div id="sponsors"><br />
<div style="margin-left:100px;"><br />
<h2>Sponsors</h2><br />
<br />
<div class="img" id="img"><br />
<div class='mask1'></div><br />
<a href="http://china.db.com/index_e.html"><img src='https://static.igem.org/mediawiki/2012/f/fc/USTC-Software-images-db-logo.png' /></a><br />
</div><br />
<br />
<div class='img' id='img'><br />
<div class='mask2'></div><br />
<a href="http://www.ustcif.org/default.php"><img src='https://static.igem.org/mediawiki/2012/d/d9/USTC-Software-images-ustcif-logo.png' /></a><br />
</div><br />
<br />
</div><!--end of up block--><br />
<br />
<div style="clear:both; margin-left:20px;"><br />
<div class='img' id='img'><br />
<div class='mask3'></div><br />
<a href="http://www.teach.ustc.edu.cn/"><img src='https://static.igem.org/mediawiki/2012/2/2d/USTC-Software-images-teaching-logo.png' /></a><br />
</div><br />
<br />
<div class='img' id='img'><br />
<div class='mask4'></div><br />
<a href="http://en.physics.ustc.edu.cn//"><img src='https://static.igem.org/mediawiki/2012/f/f5/USTC-Software-images-physics-logo.png' /></a><br />
</div><br />
<br />
<div class='img' id='img'><br />
<div class='mask5'></div><br />
<a href="http://en.biox.ustc.edu.cn/"><img src='https://static.igem.org/mediawiki/2012/2/27/USTC-Software-images-life-logo.png' /></a><br />
</div><br />
</div><!--end of bottom block--><br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-SoftwareTeam:USTC-Software2013-09-26T10:32:00Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<br />
<title>Home</title><br />
<br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/css/camera?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/css/thanks?action=raw&ctype=text/css" type="text/css"/><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<br />
<script src="https://2013.igem.org/Team:USTC-Software/js/camera?action=raw&ctype=text/javascript" type="text/javascript" charset="utf-8"></script><br />
<br />
<br />
<br />
</head><br />
<br />
<body><br />
<br />
<br />
<div id="camera"><br />
<div class="camera_wrap camera_charcoal_skin" id="camera_wrap_1" align="center"><br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/7/7c/USTC_Software_temp1.jpg<br />
" data-src="https://static.igem.org/mediawiki/2013/8/85/USTC_Software_Page1.png<br />
"><br />
<div class="camera_caption fadeFromBottom"><em>Take a gNAP before wearing your gloves!<br/>Genetic Network Analyze and Predict</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/9/99/USTC_Software_temp2.jpg<br />
" data-src="https://static.igem.org/mediawiki/2013/1/15/2.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>The sketch and final GUI of gNAP!</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/5/5b/USTC_Software_temp3.jpg<br />
data-src="https://static.igem.org/mediawiki/igem.org/0/0d/USTC_Software_three.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We compare the result of our software with gene expression profile in literature.</em></div><br />
</div><br />
<br />
<br />
<div data-thumb="https://static.igem.org/mediawiki/igem.org/a/ae/USTC_Software_temp4.jpg" data-src="https://static.igem.org/mediawiki/2013/c/cb/USTC_Software_4.jpg"><br />
<div class="camera_caption fadeFromBottom"><em>We are USTC-Software!</em></div><br />
</div><br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
<div id="green"><br />
<div class="right"><br />
<h2>Overall</h2><br />
<p align="justify">Genetic Network Analyze and Predict (gNAP) is a software which can model and analyze the change of new GRN constructing after exogenous gene’s import. What’s more, gNAP’s inverse prediction based on overall GRN gives some advise to experimenters before choosing the imported gene meeting their purpose.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Overall" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/a/a6/USTC_Software_Overall.png"/><br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
<div id="cyan"><br />
<br />
<div class="right"><br />
<h2><span class="head">Software</span></h2><br />
<p align="justify"><span style="font-family:Arial, Helvetica, sans-serif;">gNAP’s source code has been written in C++ language and the visualization parts have been written in JAVA language. By using Qt Creator, we designed our GUI which was in plain style. All of them can be compiled across platforms. There are four major parts in gNAP called Start, Monitor, Result and Display.<br/></span></p><br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Software" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6e/USTC_Software_Software.png" /><br />
</div><br />
<br />
</div><br />
<br />
<br />
<br />
<div id="blue"><br />
<br />
<div class="right"><br />
<h2>Methodologies</h2><br />
<p align="justify">In order to simulate the GRN’s working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.</p><br />
<br />
<a href="https://2013.igem.org/Team:USTC-Software/Project/Method" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d7/USTC_Software_Method.png" /><br />
</div><br />
<br />
</div><br />
<br />
<br />
<br />
<br />
<div id="purple"><br />
<br />
<div class="right"><br />
<h2><span class="head">Human Practice</span></h2><br />
<p align="justify">This year, our human practice were held on our campus. We would like to say "hi" for iGEM to every teacher and student in USTC and also bring their "Hello" to iGEM. What’s more, We hope that there will be more people who are interested in synthetic biology and programming joining in iGEM through our human practice.</p><br />
<a href="https://2013.igem.org/Team:USTC-Software/HP" class="more"></a><br />
</div><br />
<br />
<div class="left"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c0/USTC_Software_Humanpractice.png"/><br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
<div id="sponsors"><br />
<div style="margin-left:100px;"><br />
<h2>Sponsors</h2><br />
<br />
<div class="img" id="img"><br />
<div class='mask1'></div><br />
<a href="http://china.db.com/index_e.html"><img src='https://static.igem.org/mediawiki/2012/f/fc/USTC-Software-images-db-logo.png' /></a><br />
</div><br />
<br />
<div class='img' id='img'><br />
<div class='mask2'></div><br />
<a href="http://www.ustcif.org/default.php"><img src='https://static.igem.org/mediawiki/2012/d/d9/USTC-Software-images-ustcif-logo.png' /></a><br />
</div><br />
<br />
</div><!--end of up block--><br />
<br />
<div style="clear:both; margin-left:20px;"><br />
<div class='img' id='img'><br />
<div class='mask3'></div><br />
<a href="http://www.teach.ustc.edu.cn/"><img src='https://static.igem.org/mediawiki/2012/2/2d/USTC-Software-images-teaching-logo.png' /></a><br />
</div><br />
<br />
<div class='img' id='img'><br />
<div class='mask4'></div><br />
<a href="http://en.physics.ustc.edu.cn//"><img src='https://static.igem.org/mediawiki/2012/f/f5/USTC-Software-images-physics-logo.png' /></a><br />
</div><br />
<br />
<div class='img' id='img'><br />
<div class='mask5'></div><br />
<a href="http://en.biox.ustc.edu.cn/"><img src='https://static.igem.org/mediawiki/2012/2/27/USTC-Software-images-life-logo.png' /></a><br />
</div><br />
</div><!--end of bottom block--><br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T09:06:24Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Method/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<p align="justify">Without community, we can go nowhere. iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</p><br />
<br />
<p align="justify">This year our team not only devote to develop a brand new software, but also pay more attention on the “Cookbook”. For further development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. We are exited to invite you to explore with us!</p><br />
<br />
<p></br>Check it out:</p><br />
<br />
<p align="justify">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a id="out" href="http://home.ustc.edu.cn/~kun/html/index.html">online API</a></p><br />
<p align="justify">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a id="out" href="https://static.igem.org/mediawiki/2013/3/32/USTC-Software_2013_API_of_gNAP.pdf">PDF API</a></p><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T09:01:53Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Method/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<p align="justify">Without community, we can go nowhere. iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</p><br />
<br />
<p align="justify">This year our team not only devote to develop a brand new software, but also pay more attention on the “Cookbook”. For further development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. We are exited to invite you to explore with us!</p><br />
<br />
<p></br>Check it out:</p><br />
<br />
<p align="justify">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a id="out" href="">online API</a></p><br />
<p align="justify">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a id="out" href="https://static.igem.org/mediawiki/2013/3/32/USTC-Software_2013_API_of_gNAP.pdf">PDF API</a></p><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T09:01:29Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Method/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<p align="justify">Without community, we can go nowhere. iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</p><br />
<br />
<p align="justify">This year our team not only devote to develop a brand new software, but also pay more attention on the “Cookbook”. For further development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. We are exited to invite you to explore with us!</p><br />
<br />
<p></br>Check it out:</p><br />
<br />
<p align="justify">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a id="out" href="">online API</a></p><br />
<p align="justify">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a id="out" href="https://static.igem.org/mediawiki/2013/3/32/USTC-Software_2013_API_of_gNAP.pdf">PDF</a></p><br />
<br />
</div><br />
<br />
<br />
</div><br />
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<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T09:00:59Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Method/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<p align="justify">Without community, we can go nowhere. iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</p><br />
<br />
<p align="justify">This year our team not only devote to develop a brand new software, but also pay more attention on the “Cookbook”. For further development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. We are exited to invite you to explore with us!</p><br />
<br />
<p></br>Check it out:</p><br />
<br />
<p align="justify">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a id="out" href="">online API</a></p><br />
<p align="justify">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a id="out" href="https://static.igem.org/mediawiki/2013/3/32/USTC-Software_2013_API_of_gNAP.pdf">PDF</a></p><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T09:00:12Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Method/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<p align="justify">Without community, we can go nowhere. iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</p><br />
<br />
<p align="justify">This year our team not only devote to develop a brand new software, but also pay more attention on the “Cookbook”. For further development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. We are exited to invite you to explore with us!</p><br />
<br />
<p></br>Check it out:</p><br />
<br />
<p align="justify">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a id="out" href="">online API</a></p><br />
<p align="justify">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a id="out" href="https://static.igem.org/mediawiki/2013/3/32/USTC-Software_2013_API_of_gNAP.pdf">PDF</a></p><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:58:19Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Method/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<p align="justify">Without community, we can go nowhere. iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</p><br />
<br />
<p align="justify">This year our team not only devote to develop a brand new software, but also pay more attention on the “Cookbook”. For further development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. We are exited to invite you to explore with us!</p><br />
<br />
<p>Check it out:</p><br />
<br />
<p align="justify"><a id="out" href="">online API</a></p><br />
<p align="justify"><a id="out" href="https://static.igem.org/mediawiki/2013/3/32/USTC-Software_2013_API_of_gNAP.pdf">PDF</a></p><br />
<br />
</div><br />
<br />
<br />
</div><br />
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<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:57:40Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Method/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<p align="justify">Without community, we can go nowhere. iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</p><br />
<br />
<p align="justify">This year our team not only devote to develop a brand new software, but also pay more attention on the “Cookbook”. For further development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. We are exited to invite you to explore with us!</p><br />
<br />
<p>Check it out:</p><br />
<br />
<h2 align="justify"><a id="out" href="">online API</a></h2><br />
<h2 align="justify"><a id="out" href="https://static.igem.org/mediawiki/2013/3/32/USTC-Software_2013_API_of_gNAP.pdf">PDF</a></h2><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:37:23Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Method/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<h2 align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</h2><br />
<br />
<h3 align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</h3><br />
<br />
<h2 align="justify"><a id="out" href="">Click here to read the online API of gNAP !</a></h2><br />
<h2 align="justify"><a id="out" href="https://static.igem.org/mediawiki/2013/3/32/USTC-Software_2013_API_of_gNAP.pdf">Click here to download the PDF of gNAP’s API !</a></h2><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:36:44Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<script type="text/javascript" src="http://igem.stlover.org/js/meth.js"></script><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Method/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<h2 align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</h2><br />
<br />
<h3 align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</h3><br />
<br />
<h2 align="justify"><a id="out" href="">Click here to read the online API of gNAP !</a></h2><br />
<h2 align="justify"><a id="out" href="https://2013.igem.org/File:USTC-Software_2013_API_of_gNAP.pdf">Click here to download the PDF of gNAP’s API !</a></h2><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/File:USTC-Software_2013_API_of_gNAP.pdfFile:USTC-Software 2013 API of gNAP.pdf2013-09-26T08:36:19Z<p>USTCkun: </p>
<hr />
<div></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:34:08Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Method/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<h2 align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</h2><br />
<br />
<h3 align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</h3><br />
<br />
<h2 align="justify"><a id="out" href="">Click here to read the online API of gNAP !</a></h2><br />
<h2 align="justify"><a id="out" href="">Click here to download the PDF of gNAP’s API !</a></h2><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:31:25Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<h2 align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</h2><br />
<br />
<h3 align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</h3><br />
<br />
<h2 align="justify"><a id="download" href="">Click here to read the online API of gNAP !</a></h2><br />
<h2 align="justify"><a id="download" href="">Click here to download the PDF of gNAP’s API !</a></h2><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:30:45Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<h2 align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</h2><br />
<br />
<h3 align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</h3><br />
<br />
<h2 align="justify"><a id="download" href="">Click here to read the online API of gNAP!</a></h2><br />
<h2 align="justify"><a id="download" href="">Click here to download the PDF of gNAP’s API!</a></h2><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:29:12Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<br />
<h2 align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</h2><br />
<br />
<h3 align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</h3><br />
<br />
<h2 align="justify">Click here to read the online API of gNAP!</h2><br />
<h2 align="justify">Click here to download the PDF of gNAP’s API!</h2><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:28:53Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<h2 align="justify"></h2><br />
<br />
<h2 align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</h2><br />
<br />
<h3 align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</h3><br />
<br />
<h2 align="justify">Click here to read the online API of gNAP!</h2><br />
<h2 align="justify">Click here to download the PDF of gNAP’s API!</h2><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:27:55Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<h2 align="justify"></h2><br />
<p align="justify"></p><br />
<br />
<h4 align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</h4><br />
<br />
<h2 align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</h2><br />
<br />
<h2 align="justify">Click here to read the online API of gNAP!</h2><br />
<h2 align="justify">Click here to download the PDF of gNAP’s API!</h2><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:27:10Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
<br />
<br />
<div id="TT" style="margin-top:0px;"><br />
<h2 align="justify"></h2><br />
<p align="justify"></p><br />
<br />
<h2 align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</h2><br />
<br />
<h2 align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</h2><br />
<br />
<h2 align="justify">Click here to read the online API of gNAP!</h2><br />
<h2 align="justify">Click here to download the PDF of gNAP’s API!</h2><br />
<br />
</div><br />
<br />
<br />
</div><br />
<br />
<br />
</body><br />
</html></div>USTCkunhttp://2013.igem.org/Team:USTC-Software/Project/DevelopTeam:USTC-Software/Project/Develop2013-09-26T08:25:38Z<p>USTCkun: </p>
<hr />
<div>{{USTC-Software/hidden}}<br />
<br />
{{USTC-Software/header}}<br />
<br />
<html><br />
<head><br />
<br />
<title>Future Work</title><br />
<br />
<link rel="stylesheet" href="https://2013.igem.org/Team:USTC-Software/Project/Database/css?action=raw&ctype=text/css" type="text/css"/><br />
<body><br />
<br />
<div id="main"><br />
<br />
<h1>Develop</h1><br />
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<h3 align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</h3><br />
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<h3 align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</h3><br />
<br />
<h3 align="justify">Click here to read the online API of gNAP</h3><br />
<h3 align="justify">Click here to download the PDF of gNAP’s API</h3><br />
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<p align="justify">iGEM encourage every software team to update the existing software and cooperate with other teams. With the continuous development, more and more excellent softwares will be generated for the advancement of synthetic biology.</p><br />
<br />
<p align="justify">For future development and reusing of our code, we generate an API which detailedly containing the structure and function of our code. There are two ways reading it:</p><br />
<br />
<p align="justify">Click here to read the online API of gNAP</p><br />
<p align="justify">Click here to download the PDF of gNAP’s API</p><br />
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