Team:XMU Software

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<li><div onClick="team();"><a href="#_acknowledgement">Acknowledgement</a></div></li>

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<li><div onClick="project_promoter();"><a href="#">Promoter-decoder</a></div></li>

<li><div onClick="project_rbs();"><a href="#">RBS-decoder</a></div></li>

<li><div onClick="project_terminator();"><a href="#">Terminator</a></div></li>

<li><div onClick="project_protein();"><a href="#">SynoProteiner</a></div></li>

<li><div onClick="project_enote();"><a href="#">E'Note</a></div></li>

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<div class="home_s2t1">XMU-Software-2013 team consists of 11team members, 2 instructors and 3 advisors. This extraordinarily energetic and creative team is tightly connected by trust, collaboration and affection among its members. Learn more about our team on <a href="">team page.</a></div>

</br><div class="home_s2t1">Our project includes 2 independent software tools-the brick worker and E’NOTE. The former is a software suit for the evaluation and optimization of biobricks, i.e., promoter, RBS, protein coding sequence and terminator. E’NOTE is a web application serveing as an assistant for experiments. It’s useful functions such as experiments recording and experimental template customization make experimental process easier and more enjoyable. To learn more about our project, please visit <a href="">Project page.</a></div>

</br></br><div class="home_s2t1">See more about XMU-Software-2013 safety form on <a href="#"><span onClick="safety();">safetyform page</span></a></div>

</br></br></br></br><div class="home_s2t1">XMU-Software-2013 has hosted a series of activities aiming at facilitating communication and collaboration among iGEM teams, propagating iGEM competition as well as promoting the development of synthetic biology. And these efforts are well paid off and have received satisfying feedback. More information is available at <a href="#"><span onClick="hp();">Outreach page.</span></a></div>

</br></br></br><div class="home_s2t1">See more about XMU-Software-2013 Notebook on <a href="">Notebook page.</a></div>

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<div class="team_title">TEAM</div>

<div class="team_introduce">XMU-Software-2013 team consists of 11team members, 2 instructors and 3 advisors. This extraordinarily energetic and creative team is tightly connected by trust, collaboration and affection among its members.</div>

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<a id="team_navi1" class="navi2" href="#_ourteam"          onmouseover="navi1();" onmouseout="navi11();">Our Team</a>

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<div class="body_heading" id="team_m2t1">Members</div>

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<div id="T12"><div class="yk">Members</div></div>

<div ><img class="wcleft" id="w1" src="https://static.igem.org/mediawiki/igem.org/d/d7/Xmusoftware_%E9%BB%84%E6%96%B0%E6%96%87%E6%A1%A31.png "></div>

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<div ><img class="w2left" id="ww2" src="https://static.igem.org/mediawiki/igem.org/8/87/Xmusoftware_%E6%9E%97%E5%B1%BE%E6%96%87%E6%A1%A32.png"></div>

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<div ><img class="wcright" id="w11"src="https://static.igem.org/mediawiki/igem.org/5/50/Xmusoftware_%E9%BB%84%E5%89%91%E5%85%B4%E6%96%87%E6%A1%A31.png "></div>

<div ><img class="w2right" id="ww3" src="https://static.igem.org/mediawiki/igem.org/0/07/Xmusoftware_%E9%82%B1%E7%AB%8B%E6%A5%B7%E6%96%87%E6%A1%A32.png"></div>

<div ><img class="w2right"id="ww5" src="https://static.igem.org/mediawiki/igem.org/7/70/Xmusoftware_%E9%99%88%E6%9C%88%E6%A1%A2%E6%96%87%E6%A1%A32.png"></div>

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<div ><img class="w3right" id="www9"src="https://static.igem.org/mediawiki/igem.org/8/84/Xmusoftware_%E9%A5%B6%E6%96%87%E5%86%9B%E6%96%87%E6%A1%A33.png"></div>

<div ><img class="w3right" id="www11"src="https://static.igem.org/mediawiki/igem.org/d/d5/Xmusoftware_%E9%BB%84%E5%89%91%E5%85%B4%E6%96%87%E6%A1%A33.png"></div>

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<div class="body_heading">Instructors</div>

<div id="team_m3n1">

<div><img src="https://static.igem.org/mediawiki/igem.org/5/5e/Xmusoftware_Instructor1.jpg"></div>

<div class="team_m3t1">

<span class="body_title">Prof. Bai-shan Fang</span></br></br>

<span class="body_text">Dr. Bai-shan Fang is the professor in College of Chemistry &amp; Chemical Engineering at Xiamen University. In the group of biocatalysis and biotransformation, his research mainly focuses on synthetic biology, mining and the transformation of the enzyme, construction of bio-molecular machines, the application of new biocatalysts. His major role of XMU iGEM team is to mentor for all and to enlighten the members. </br> </br></br> </br></br> </br></br></span>

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<div class="body_heading">Advisors</div>

<div><img src="https://static.igem.org/mediawiki/igem.org/7/76/Xmusoftware_Advisor1.jpg"></div>

<div class="team_m3t1">

<span class="body_title" >Ruosang Qiu</span></br></br>

<span class="body_text">It is great for a team to have an omniscient advisor, even better an inspiring one. Ruosang Qiu , our beloved advisor, definitely is offering both. Her hard work as well as undoubted adorable personality is the motivation of our team members’ efforts, her clear mind combined with provident planning lays the foundation of our successful project, To quote her words: I’ was a happy iGEMer in 2012, I’m going to make you all happy iGEMers in 2013.</br></br> </br></br> </br></br></span>

<span class="body_text">The past three years have seen his tremendous dedication to iGEM, Xin Wu, a passionate team member in XMU-China-2011, a devoted team leader in XMU-China-2012 and now, an invaluable advisor in XMU-Software-2013. Had it not been Xin Wu's constant encouragement and guidance, we,the inexperienced iGEMers might have been faint-hearted and failed to face up to the challenges during the process. It is drawing on his expertise in synthetic biology and proficiency in iGEM competition that we have solved the seemingly unsolvable and conquered the seemingly unconquerable.</br></br></br></br></br></span>

<span class="body_text">Youbin Mo is one of the great advisors of XMU software team in 2013. As a computational biophysicist, y, he is an unquestionable master of biological model and computer programing. In the meanwhile, website constructing is also Mo’s technical ability which he acquired by participating the iGEM last year. Youbin gives play to his talent by teaching fundamental program skills to new iGEMers as well as directing them to be self-reliant synthetic biologists.</br></br> </br></br> </br></br></span>

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<div id="acknowledgement"><a name="_acknowledgement" id="_acknowledgement"></a>

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<div class="sep_title">ACKNOWLEDGEMENT</div>

<div class="sep_number">02</div>

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All work described on this wiki or on our parts registry pages was done by iGEM Team XMU-China 2012. We managed the whole project, from planning to financing the complete lab work by ourselves. Nevertheless we could not have done all this work without the help, advice and guidance of several people. Therefore, special thanks to the following people:</br>

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<div class="sep_title">OUR CITY&amp;UNIVERSITY </div>

<div class="sep_number">03</div>

<span class="body_title" >Xiamen</span></br></br>

<span class="body_text">Xiamen, also known as Amoy to the west, is a cozy city located in the southeastern part of China, and has a relaxing coastal charm with a population of 1.3 million. It's a historical harbor city which was founded in the mid-14 century, in the early years of the Ming Dynasty. In the early 1980's, Xiamen was declared as one of China's first Special Economic Zone, taking advantages of the city's heritage as a trading center and the proximity to Taiwan. In 2004 the city won the finals of the world's Human Settlements and Environment Award, "Nations in Bloom". Xiamen is one of China's most attractive and best-maintained resort city, and attracts a large number of foreign and local tourists. The city is easily accessible by air, and there are direct flights from Hong Kong, Kuala Lumpur, Osaka, Seoul, Singapore and Tokyo. Within China, Xiamen airport is linked to more than 30 domestic airports.</span>

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<span class="body_title" >Xiamen University</span></br></br>

<span class="body_text">Xiamen University (XMU), also known as Universitas Amoiensis in Latin, is one of the top universities in China. It was founded in 1921 by Tan Kah-Kee, the well-known patriotic overseas Chinese leader. As an integrated university, XMU owns a comprehensive branches of discipline as well as many specialized institutes. Economy, counting, chemistry, life science and marine science all win high fame nationwide and even worldwide. The main campus of XMU locates in a picturesque setting between the sea and a scenic mountain, spreading over 150 hectares, and is generally regarded as the most beautiful campus in China.</span>

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<div class="project_title" id="project_s1m1">PROJECT</div>

<div class="project_introduce">Our project includes 2 independent software tools-the brick worker and E’NOTE. The former is a software suit for the evaluation and optimization of biobricks, i.e., promoter, RBS, protein coding sequence and terminator. E’NOTE is a web application serveing as an assistant for experiments. It’s useful functions such as experiments recording and experimental template customization make experimental process easier and more enjoyable. To learn more about our project, please visit Project page.</div>

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<a id="project_navi1" class="navi1" href="#_promoter"          onClick="project_promoter();"  onmouseover="navi1();" onmouseout="navi11();">Promoter-decoder</a>

<a id="project_navi2" class="navi2" href="#_rbs"              onClick="project_rbs();"      onmouseover="navi2();" onmouseout="navi21();">RBS-decoder</a>

<a id="project_navi3" class="navi3" href="#_terminator"        onClick="project_terminator();"onmouseover="navi3();" onmouseout="navi31();">Terminator</a>

<a id="project_navi4" class="navi4" href="#_protein"          onClick="project_protein();"  onmouseover="navi4();" onmouseout="navi41();">SynoProteiner</a>

<a id="project_navi5" class="navi5" href="#"                   onClick="project_enote();"     onmouseover="navi5();" onmouseout="navi51();">E' Note</a>

<a id="project_navi6" class="navi6" href="javascript:void(0)"                                onmouseover="navi6();" onmouseout="navi61();">Back To Top</a>

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<div class="sep_title">Terminator</div>

<div class="sep_number">03</div>

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<div class="body_heading">Background</div>

<div class="body_text">Gene  expression  in  both  prokaryotes  and  eukaryotes  is  frequently  controlled  at  the  level  of  transcription.  This  process  can  be represented  as a cycle  consisting  of  four  major  steps:  (1)  promoter binding;  (2)  RNA  chain  initiation;  (3)  RNA  chain elongation;  and  (4) termination. Since regulatory controls are exerted at each step, an understanding of the mechanism of each step is of general importance in understanding gene expression.</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/c/c6/Xmusoftware_Ter1.jpg"></div>

<div class="body_text">The energy of potential hairpin configurations adjacent to a reference position can be found efficiently with a dynamic programming algorithm. The table entry hairpin_score[i,j] gives the cost of the best hairpin structure for which the base of the 5' stem is at nucleotide position i and the base of the 3' stem is at position j. The entry hairpin_score[i,j] can be computed recursively as follows:</div>

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<div class="body_heading">Abstract</div>

<div class="body_text">RBS-decoder is a software tool for evaluating the relative strength of RBS sequence. This program use the same method—PWM to calculate the similarity between RBS sequence and the position frequency matrix of SD sequence and transform the similarity to the relative strength of an RBS sequence.</div>

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<div class="body_heading">Background</div>

<div class="body_text">Translational efficiency in Escherichia coli is generally determined at the stage of initiation. There are several principal mRNA sequence elements which can affect the kinetics of ternary initiation complex formation (30 S-mRNA-fMet-tRNA): the initiation codon, the Shine-Dalgarno (SD) sequence, the spacing between those two elements, and other factors.</div>

<div class="body_text">The bacterial ribosome binds to particular sequences on an mRNA, primarily the Ribosome Binding Site (RBS) and the start codon (ATG). The RBS base-pairs with an RNA molecule that forms part of the bacterial ribosome (the 16s rRNA), while the start codon base-pairs with the initiator tRNA which is bound to the ribosome. In addition to the sequences of the RBS and the start codon being important, these two sequences need to be positioned approximately 6-7 nucleotides apart so they can both make contact with the appropriate parts of the ribosome complex1.</div>

<div class="body_text">The Shine-Dalgarno Sequence</div>

<div class="body_text">The RBS sequence logo representing the sequences of 149 RBS from E. coli. The height of each letter represents the frequency of the base at that location. From Tom Schneider, "A Gallery of Sequence Logos".</div>

<div class="body_text">The end of the 16s rRNA that is free to bind with the mRNA includes the sequence 5′–ACCUCC–3′. The complementary sequence, 5′–GGAGGU–3′, named the Shine-Dalgarno sequence, can be found in whole or in part in many bacterial mRNA. Very roughly speaking, ribosome binding sites with purine-rich sequences (A's and G's close to the Shine-Dalgarno sequence will lead to high rates of translation initiation whereas sequences that are very different from the Shine-Dalgarno sequence will lead to low or negligible translation rates. The sequence is named after the researchers who discovered the high frequency of the sequence upstream of bacterial coding sequences . You can see how common the sequence is by looking at the RBS sequence logo on the right (where the height of a letter indicates the frequency of the letter at that location).</div>

<div class="body_text">As we know, the RBS strength is greatly influenced by the SD sequence, where the 16s RNA of the ribosom binds to, so the strength can be determined by the free binding energy between the SD sequence and the 16s RNA. So we designed a program calculating the free binding energy but the results show that the correlation between the free energy and the strength of RBS is rather weak (R2=0.5517). So we decide to find other algorithms for better accuracy.</div>

<div class="body_text">Inspired by the strength prediction algorithms used in promoter part, in which the similarity to the sigma factors’ PWM is interlocking with the binding affinity between the protein and DNA sequence. We obtained the Position Frequency Matrix of SD sequence of E.coli and use the PMW method (illustrated in detail in the promoter part) to calculate the similarity between the RBS sequence and the Position frequency sequence, what is different from the promoter is that, the spacer length between the SD sequence and the startcodon and the start codon itself both act as constraints in locating the SD sequence, which is confined to 3-16 bp and ATG/TTG/GTG. And similar to the prediction of promoter strength, the spacer length between the SD sequence also contribute to the RBS strength, the weight of the influence of the spacer on strength is determined by our experiments in which we either mutated the SD sequence in a RBS sequence or change the spacer length to figure out the relative influence of these two factors 2.</div>

<div class="body_text">We use the RBS sequence listed on the iGEM registry with experimentally determined relative strength, and the correlation between the RBS strength predicted by our software and the actual relative strength is strong, with a determination coefficient value 0.8039.</div>

<div class="body_text">Due to scarcity of experimental data, the relative weight of the SD sequence and the spacer length used currently is roughly determined which may undermine the accuracy of RBS strength prediction. For further improvement of our program, we’ll try to obtain more reliable experimental data to accurately determine the weight used in our algorithm and hopefully elevate the accuracy of RBS-decoder.</div>

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<div id="promoter"><a name="_promoter" id="_promoter"></a>

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<div class="sep_title">Promoter-decoder</div>

<div id="promoter_m2">

<div class="body_heading">Abstract</div>

<div class="body_text">Pro-decoder is part of our software designed to analyze and evaluate promoters. The software use PWM method to calculate the similarity between promoter sequences and the position frequency matrix of transcription factor binding sites (TFBS) to locate the TFBS as well as predict the relative strength of the promoter. So the user may have a better understanding of the promoter’s regulation mechanism and the key sites which influences the promoter’s performance mostly.</div>

<div class="body_text">Bacteria encode several thousands of different proteins, which are necessary for normal cell functions or for adaptation to environmental changes. These proteins are not required at the same time or in the same amount. Regulation of gene expression therefore enables the cell to control the production of proteins needed for its life cycle or for adaptation to extracellular changes. This regulation in turn makes it possible for the bacterium to adequately adapt to rapid changes in the environment. The various steps during transcription and translation are therefore subject to different regulatory mechanisms. The most prominent step in gene regulation is the initiation of transcription in which the DNA-dependent RNA polymerase (RNAP) is the key enzyme. The RNAP or the RNAP core enzyme is the catalytic machinery for the synthesis of RNA from a DNA template. However, RNAP cannot initiate transcription by itself. Initiation of transcription requires an additional polypeptide known as a sigma-factor. Sigma-factors are a family of relatively small proteins that can associate in a reversible way with the RNAP core enzyme. Together, the sigma-factor and the RNAP core enzyme form an initiation-specific enzyme, the RNAP holoenzyme</div>

<div class="body_text">The sigma-factor directs RNA polymerase to a specific class of promoter sequences. Most bacterial species synthesize several different sigma-factors that recognize different consensus sequences. This variety in sigma-factors provides bacteria with the opportunity to maintain basal gene expression as well as for regulation of gene expression in response to altered environmental or developmental signals.</div>

<div class="body_text">The frequency at which the RNAP holoenzyme initiates transcription, also known as the strength of a promoter, is influenced by the promoter sequence and the conformation of the DNA in the promoter region. The sigma-factors recognize two conserved sequences in the promoter region, known as the promoter consensus sequence. Sigma-factors or fragments of sigma-factors bind specifically to promoter DNA sequence and by specific base pair and amino acid substitutions in the promoter consensus sequences or sigma factors. Most bacterial species synthesize several different sigma-factors which direct the RNAP holoenzyme to distinct classes of promoters with a different consensus sequence. This variety in sigma-factors provides the bacterium with the opportunity to maintain basal gene expression as well as for regulation of gene expression in response to specific environmental stimuli.</div>

<div class="body_text">The identification of bacterial promoters is an essential step in the elucidation of gene regulation. As a general rule, the more complex the life-cycle and environmental niche of a bacterium, the greater the number of sigma factors with corresponding promoter types. Typically however, the most common promoter type is that which regulates the housekeeping genes and the corresponding major sigma-factor is shared by all bacteria (sigma 70 in the well studied Escherichia coli, and its homologues in other species). The binding site for the sigma70-family of promoters is defined by two consensus hexamers, TTGACA and TATAAT, located at approximately −35 and −10, respectively relative to the transcript start site (TSS) and spaced 15–21 base pairs (bp) apart2. RNA polymerase core enzyme associates with the major sigma-factor to form the holoenzyme which in turn binds to its cognate promoters to initiate transcription.</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/8/89/Xmusoftware_promoter2.jpg"><img src="https://static.igem.org/mediawiki/igem.org/b/b0/Xmusoftware_promoter3.jpg"></div>

<div class="body_text">In prokaryotes, the minimum requirement for RNA polymerase binding is recognition of the promoter by the sigma factor. In general, prokaryotic RNA polymerases can interchange a number of sigma factors which bind and initiate different groups of genes³.</div>

<div class="body_title">Transcription Factors</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/c/c3/Xmusoftware_promoter4.jpg"></div>

<div class="body_text">Sigma factors are essential for the transcription initiation in Escherichia coli. In addition, promoter strengths are not determined purely by the binding of the sigma factor. other transcription factors can bind specific sequences surrounding or overlapping the promoter to either activate or repress transcription4. The mechanism is Transcriptional activators and repressors contribute to and detract from the accessibility of DNA by the RNA polymerase. These transcription-regulating nuclear proteins bind to specific binding sites in the regulatory regions (e.g. promoters, enhancers) of the genes thus providing their activation or repression. </div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/0/01/Xmusoftware_promoter5.jpg"></div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/9/93/Xmusoftware_promoter6.jpg"></div>

<div class="body_text">Computational methods of predicting TF binding sites in DNA are very important for understanding the molecular mechanisms of gene regulation.</div>

<div class="body_text">The binding sites of the same transcription factor show a significant sequence conservation, which is often summarized as a short (5–20 bases long) common pattern called a transcription factor binding site (TFBS) or binding consensus. Our software aims to figure out the possible TFBS in promoters and precisely locate the TFBS so that the user may know the exact sites that play a role in regulating the transcription.</div>

<div class="body_text">In prokaryotes (lower organisms without nuclei), there are fewer TFs, their motifs tend to be relatively long and the strength of regulation for a particular gene often depends on how closely a particular site matches the consensus for the motif. The more mismatches to the consensus in a binding site, the less often the TF will bind and therefore the less control it will exert on the target gene. So our software will calculate the similarity between the possible TFBS in the promoter and the standard motifs so the user will know to which extent the transcription factor will control the promoter transcription</div>

<div class="body_title">Primer design</div>

<div class="body_text">To facilitate the design of PCR primers of various promoters, we’ve developed an additional function, namely, primer design in this part og our program. After inputting the promoter sequence, the software will figure out the most suitable primers based on the theory of Thomas K¨ampke1, Markus Kieninger, and Michael Mecklenburg.</div>

</br>

<div class="body_heading">Data source</div>

<div class="body_title">RegulonDB</div>

<div class="body_text">Genes and operons that are under control of the same TF are members of that TF’s regulon.  Although methods for the prediction of regulons have been substantially improved, they are still far from perfect. </div>

<div class="body_text">Comparative genomics tools can be used to predict regulons in bacterial genomes but the procedure can lead to incorrect regulon calling. Despite this drawback, several regulon databases are available that are based on comparative genomics methods and lack experimental evidence.</div>

<div class="body_text">Probably the extended and accurate databases of regulons for E.coli are RegulonDB which provides the data source for our program.</div>

<div class="body_heading">Algorithm</div>

<div class="body_text">PWM (Position Weight Matrix)</div>

<div class="body_text">Molecular techniques for the identification of promoters are both costly and time consuming, hence in silico methods are an attractive and well explored alternative. The most common in silico method to identify _70 promoters uses position weight matrices (PWMs) and depends on the relative conservation of the transcription factor binding site (TFBS, or motifs ). </div>

<div class="body_text">The algorithm can be divided into two parts regarding to the difference between the motifs of sigma factors and other transcription factors.  </div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/7/71/Xmusoftware_promoter7.jpg"></div>

<div class="body_title">Part 1: the recognition of other transcription factors6</div>

<div class="body_text">Other transcription factors are proteins that can bind to a specific DNA sequence (motifs) and regulate the promoter’s transcription. To recognize these possible motifs in a given promoter sequence, we calculate the Matrix Similarity Score (MSS) of every possible sites in the promoter sequence using the position frequency matrix of 86 transcription factors published by RegulonDB. The algorithm reports only those matches of a matrix that have got MSS higher than the settled threshold.  And MSS for a subsequence x of the length L is calculated in following steps:</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/e/ee/Xmusoftware_promoter8.jpg"><img src="https://static.igem.org/mediawiki/igem.org/1/16/Xmusoftware_promoter9.jpg"></div>

<div class="body_text">fi,B, frequency of nucleotide B to occur at the position i of the matrix (B ∈{A, T, G, C})</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/0/0e/Xmusoftware_promoter10.jpg"></div>

<div class="body_text">fimin, frequency of the nucleotide which is rarest in position i in the matrix</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/f/ff/Xmusoftware_promoter11.jpg"></div>

<div class="body_text">fimax, highest frequency in position i.</div>

<div class="body_text">The information vector</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/c/c3/Xmusoftware_promoter12.jpg"></div>

<div class="body_text">describes the conservation of the positions i in a matrix (5).Multiplication of the frequencies with the information vector leads to a higher acceptance of mismatches in less conserved regions, whereas mismatches in highly conserved regions are very much discouraged. This leads to a better performance in recognition of TF binding sites if compared with methods that do not use the information vector.</div>

<div class="body_text">To determine the best threshold of the motif finding algorithm, we test various threshold values and analyze the true negative and false positive rate of each threshold value. The ideal threshold is supposed to have both the least true negative and false positive rates.</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/c/c0/Xmusoftware_promoter13.jpg"></div>

<div class="body_text">The picture above shows part of our test results and to keep both the true negative and false positive rates at a reasonable level, we adopt 3 threshold values, namely, low (0.5977) , median (0.0778) and high (0.85), with a true negative rate at 0.1, 0.5, and 0.7 respectively. And for more flexibility, we also allow the users to set their own thresholds.</div>

<div class="body_title">Part2: the recognition of sigma factor motif and the evaluation of relative promoter strength.</div>

<div class="body_text">In the case of sigma 70 factors, the motifs are the −35 and −10 hexamers. enclosing a spacer of length 15–19 bp.</div>

<div class="body_text">(i) That the spacer length (the number of base pairs between the −35 hexamer and the −10 hexamer) should lie in the range {14–20};</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/6/62/Xmusoftware_promoter14.jpg"></div>

<div class="body_text">(ii) the total MSS  (our results are the sum of the scores for the −10 and −35 hexamers and therefore lie in the interval [0,2], with a score of 2 corresponding to the joint consensus TTGACA (−35) and TATAAT (−10).</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/5/57/Xmusoftware_promoter15.jpg"></div>

<div class="body_text">In prokaryotes, the strength of sigma factor regulation for a particular gene often depends on how closely a particular site matches the consensus for the motif. The more mismatches to the consensus in a binding site, the less often the sigma factor will bind and therefore the less strength the promoter will have. Experiments have confirmed the hypothesis that the consensus promoter sequence is “best”. We set the best promoter strength to 100% and calculate the relative strength of a given promoter by the Score (promoter)</div>

<div class="body_title">Primer design</div>

<div class="body_text"> A primer pair (p, q) is assigned the scoring vector</div>

<div class="body_text">sc(p, q) = (|p|, |q|,GC(p),GC(q), Tm(p), Tm(q), sa(p),sa(q), sea(p), sea(q), pa(p, q), pea(p, q))T ∈ R12</div>

<div class="body_text">All primers are designed to have ideal values of length,GC content, and melting temperature which are specified externally by the designer of the hybridization experiment. These ideal values are to be specified for forward and reverse primers. The ideal score vector or reference vector for the primer pair is </div>

<div class="body_text">&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp&amp;nbsp

sc_ideal =(length_f , length_r,<i>GC_f ,GC_r , T_m,f ,T_m,r ,</i> 0, 0, 0, 0, 0, 0)^T.</div>

<div class="body_text">All ideal annealing values are set to zero and typically</div>

<div class="body_text">Tm_f= T_m,ras well as GC_f = GC_r . The final assessment of a primer pair (p, q) can be its deviation from the reference in terms of the l1-distance</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/0/09/Xmusoftware_promoter16.jpg"></div>

<div class="body_text">Here, we employ a weighted distance</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/7/70/Xmusoftware_protein17.jpg"></div>

<div class="body_text">with weights given in the following table.</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/a/af/Xmusoftware_protein18.jpg"></div>

<div class="body_text">The formulas for calculating the variations above are provided in <Efficient primer design algorithms>⁷</div>

</br>

<div class="body_heading">Results</div>

<div class="body_title">Sigma factors recognition </div>

<div class="body_text">To test the ability of our program to recognize the exact type of promoters, i.e., to nail down by which sigma factor a promoter is regulated, we run our program with 100 various promoters sequences whose type have already confirmed experimentally. And we’ve recognized 56 of them correctly. The results are showed below.</div>

</br>

<div class="body_heading">TFBS Location</div>

<div class="body_text">Specifically, we use the sigma70 promoter sequence with annotated -35 and -10 region provided by RegulonDB to test the correct prediction rate of the binding site of a specific transcription. We input 89 sigma70 promoter sequences and run our program to precisely locate the sigma factor binding site. Results show that the correct site prediction rate of our program is 64%.</div>

<div class="body_text">The test results are as follows. the numbers represent the site of actual-35 motif, the actual spacer length, the predicted site and predicted spacer length respectively.</div>

<div class="body_title">Promoter strength correlation</div>

<div class="body_text">Making an attempt to predict the promoter strength through motif similarity and spacer length is one of our software’s shine points. We input XX promoter sequences with experimentally determined strength data. And the result shows that the determination coefficient is X.</div>

<div class="body_heading">Future work</div>

<div class="body_text">Apply our algorithms to more species. Now Pro-decoder is designed expressly for prediction and evaluation of E.coli promoters, in the near future we’ll study the transcription regulation mechanism of other species and try to apply our algorithms to a extended range of species.</div>

<div class="body_text">Enhance promoter strength prediction accuracy. Because our experimental data is so limited, the weight of the spacer length and the motif similarity is roughly determined, which lead to a weak correlation between the promoter strength. In the future we hope to obtain more experimental data with regard to the effect of spacer length and motif similarity having on promoter strength so we can revise the weight coefficient of the two factors and get more reliable results.</div>

<div class="body_text">The next version of this part of our program will be able to analyze not only the promoters of E.coli, but other species such as Bacillus subtilis, we’ll integrate the transcription factor binding site data of more species into our database and use PWM algorithm to predict the TFBS in the promoters.</div>

<div class="body_heading">References</div>

<div class="body_references">1. Wösten, M., Eubacterial sigma‐factors. <i>FEMS microbiology reviews</i> 1998,<i> 22 (3),</i> 127-150.</div>

<div class="body_references">2. Shultzaberger, R. K.; Chen, Z.; Lewis, K. A.; Schneider, T. D., Anatomy of Escherichia coli σ70 promoters. <i>Nucleic acids research</i> 2007, <i>35 (3),</i> 771-788.</div>

<div class="body_references">3. Paget, M.; Helmann, J. D., The sigma70 family of sigma factors.<i> Genome Biol</i> 2003,<i> 4 (1),</i> 203.</div>

<div class="body_references">4. Jensen, S. T.; Liu, X. S.; Zhou, Q.; Liu, J. S., Computational discovery of gene regulatory binding motifs: a Bayesian perspective. <i>Statistical Science</i> 2004, <i>19 (1),</i> 188-204.</div>

<div class="body_references">5. (a) Rhodius, V. A.; Mutalik, V. K., Predicting strength and function for promoters of the Escherichia coli alternative sigma factor, σE. Proceedings of the National Academy of Sciences 2010, 107 (7), 2854-2859; (b) Mulligan, M. E.; Brosius, J.; McClure, W. R., Characterization in vitro of the effect of spacer length on the activity of Escherichia coli RNA polymerase at the TAC promoter. Journal of Biological Chemistry 1985, 260 (6), 3529-3538; (c) Qureshi, S. A.; Jackson, S. P., Sequence-Specific DNA Binding by the&lt;i> S. shibatae</i> TFIIB Homolog, TFB, and Its Effect on Promoter Strength. <i>Molecular cell</i> 1998,<i> 1 (3),</i> 389-400.</div>

<div class="body_references">6. Kel, A. E.; Gößling, E.; Reuter, I.; Cheremushkin, E.; Kel-Margoulis, O. V.; Wingender, E., MATCHTM: a tool for searching transcription factor binding sites in DNA sequences. <i>Nucleic acids research</i> 2003, <i>31 (13),</i> 3576-3579.</div>

<div class="body_references">7. Kämpke, T.; Kieninger, M.; Mecklenburg, M., Efficient primer design algorithms.<i> Bioinformatics</i> 2001,<i> 17 (3),</i> 214-225.</div>

</div>

<div id="protein"><a name="_protein" id="_protein"></a>

<div id="protein_m1">

</br></br>

<div class="body_heading">Abstract</div>

<div class="body_text">Our software team mainly focuses on programming the software by two methods using genetic algorithm, evaluating both optimization of single codon and codon pair and hence determining the fittest optimized sequence for expression in heterologous host cell.</div>

<div class="body_text">After choosing the method and deciding the host cell, user inputs an original encoding sequence. By substitute the synonymous codons in the sequence, a new sequence is generated. Caluculate the fitness value and choose between the two sequences under the rule that the larger the value is, the higher the possibility of being chosen will be. The chosen sequence will be regarded as the original encoding sequence and repeat the steps until iteration stop criterion. By the way, user can click the “values” button to input the values needed in genetic algorithm (default value: population size—200, hereditary algebra—100, mutation probability--0.1, crossover probability--0.1).</div>

</br>

<div class="body_heading">Background</div>

<div class="body_text">Except methionine and tryptophan, all amino acids can be encoded by two to six synonymous codons, resulting from the degeneracy of the genetic code.1  However, unequal utilization of the synonymous condons leads to the phenomenon of codon usage bias, which is mainly due to natural selection, mutation and genetic drift.2 According to related studies, codon usage bias has certain connection with gene expression level. 3The larger the value of codon usage bias is, the higher gene expression will be. So the problem, how to substitute the synonymous codons aimed at raising the efficiency of gene expression and thus increasing the production of recombination protein in heterologous host cell, is expected to be addressed.</div>

<div class="body_heading">Introduction</div>

<div class="body_title">Balance with single codon and codon pair</div>

<div class="body_text">Individual codon usage optimization has been attached importance to, taking Codon optimizer4, Gene Designer5, OPTIMIZER6 for example. Subsequently, people found the effect of gene expression optimization cannot be perfect just by single codon optimization. Codon pair, namely the pair of k-th and (k+1)-th codons from the 5’ to 3’ end, is another crucial factor. Due to potential tRNA-tRNA steric interaction within the ribosomes7, the usage of rare condon pairs, which correlate with translation elongation, decrease protein translation rates8. Optimization of individual codon has an influence on the corresponding codon pair resulting in maybe-not-the-best codon pair optimization. In the same way, optimizing codon pair merely contributes to maybe-not- the-best single codon optimization. Therefore, it is a challenging way for us to apply a method considering and weighing the effects of single codon and codon pair optimization and thus make the whole best.</div>

<div class="body_text">Our software team mainly focuses on programming the software by two methods using genetic algorithm, evaluating both optimization of single codon and codon pair and hence determining the fittest optimized sequence for expression in heterologous host cell. </div>

<div class="body_title">Host Cell</div>

<div class="body_text">Considering E. coli and S. cerevisiae are the ideal hosts for recombinant proteinexpression, and Gram-positive bacterium L.lactis and methylotrophic yeast P.pastoris are also promising candidates for expressing recombinant proteins9, we attached importance to selecting these four kinds of bacterium as host cell to optimize the sequence.</div>

<div class="body_heading">Algorithm</div>

<div class="body_title">Part I—method I: Fitness10</div>

<div class="body_text">Fitness function：</div>

<div class="body_photo"><img src="images/protein1.jpg"></div>

<div class="body_text">In the function,</div>

<div class="body_photo"><img src="images/protein2.jpg"></div>

<div class="body_text">cpi is a value larger than zero, ranging from 10^-4 to 0.5，fit_cp(g) is the fitness function of the codon pair，fit_sc(g) is the fitness function of the single codon，w((c(k)，c(k+1)) is the weight of codon pairs in sequence g，|g| is the length of encoding sequence，c(k) is k-th codon in the sequence，<img src="images/protein3.jpg"> is the target ratio of k-th codon, <img src="images/protein4.jpg"> is the actual ratio of k-th codon in the sequence，the best value of cpi is 0.2 in the software.</div>

<div class="body_text">In the function, the target ratio of k-th codon can be approximated by the equation below:</div>

<div class="body_photo"><img src="images/protein5.jpg"></div>

<div class="body_text">In the function, weight can be calculated by the equation below:</div>

<div class="body_photo"><img src="images/protein6.jpg"></div>

<div class="body_photo"><img src="images/protein7.jpg"></div>

<div class="body_text"><img src="images/protein8.jpg">stands for the ratio of single codon c_kin the complete genome'<img src="images/protein9.jpg">is the number of pair (c_i,c_j)  in high-expression genes，and high-expression genes are genes whose copy numbers of mRNA can be detected at least 20 per cell. </div>

<div class="body_photo"><img src="images/protein10.jpg"></div>

<div class="body_text">syn(c_k)stands for the synonymous codon set related to c_k,<img src="images/protein11.jpg">equals to the number of amino acid encoded by c_i in the whole protein set. </div>

<div class="body_text">Based on the table below, we calculate function of single codon (ICU) , function of codon pair (CCO) and function of the combination of two (MOCO). MOCO aims at make the optimization of whole best by calculating the relative effect of ICU and CCO. </div>

<div class="body_photo"><img src="images/protein12.jpg"></div>

<div class="body_photo"><img src="images/protein13.jpg"></div>

<div class="body_photo"><img src="images/protein14.jpg"></div>

<div class="body_photo"><img src="images/protein15.jpg"></div>

<div class="body_title">Calculation of ICU</div>

<div class="body_text">max&amp;nbsp&amp;nbsp&amp;nbsp <img src="images/protein16.jpg"></div>

<div class="body_text">s.t.&amp;nbsp&amp;nbsp&amp;nbsp <img src="images/protein17.jpg"></div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/a/af/Xmusoftware_protein18.jpg"></div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/5/5a/Xmusoftware_protein19.jpg"></div>

<div class="body_title">Calculation of CC</div>

<div class="body_text">max&amp;nbsp&amp;nbsp&amp;nbsp <img src="https://static.igem.org/mediawiki/igem.org/a/a7/Xmusoftware_protein20.jpg"></div>

<div class="body_text">s.t.&amp;nbsp&amp;nbsp&amp;nbsp <img src="https://static.igem.org/mediawiki/igem.org/0/08/Xmusoftware_protein21.jpg"></div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/a/a6/Xmusoftware_protein22.jpg"></div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/c/cb/Xmusoftware_protein23.jpg"></div>

<div class="body_title">Calculation of MOCO</div>

<div class="body_text">The MOCO problem is as follows (NSGA-II algorithm applied):</div>

<div class="body_text">1. Randomly initialize a population of coding sequences for target protein.</div>

<div class="body_text">2. Evaluate ICU and CC fitness of each sequence in the population.</div>

<div class="body_text">3. Group the sequences into nondominated sets and rank the sets.</div>

<div class="body_text">4. Check termination criterion.</div>

<div class="body_text">5. If termination criterion is not satisfied, select the “fittest” sequences (top 50% of the population) as the parents for creation of offsprings via recombination and mutation.</div>

<div class="body_text">6. Combine the parents and offsprings to form a new population.</div>

<div class="body_text">7. Repeat steps 2 to 5 until termination criterion is satisfied.</div>

<div class="body_text">The identification and ranking of nondominated sets in step 3 is performed via pair-wise comparison of the sequences’ ICU and CC fitness. For a given pair of sequences with fitness values expressed as <img src="images/protein24.jpg"> and <img src="images/protein25.jpg"> , the domination status can be evaluated as follows:</div>

<div class="body_text">• If  <img src="https://static.igem.org/mediawiki/igem.org/f/fb/Xmusoftware_protein26.jpg"> ><img src="https://static.igem.org/mediawiki/igem.org/1/16/Xmusoftware_protein27.jpg">  and <img src="https://static.igem.org/mediawiki/igem.org/7/78/Xmusoftware_protein28.jpg"> >=<img src="https://static.igem.org/mediawiki/igem.org/f/ff/Xmusoftware_protein29.jpg">  , sequence 1 dominates sequence 2.</div>

<div class="body_text">• If  <img src="https://static.igem.org/mediawiki/igem.org/f/fb/Xmusoftware_protein26.jpg"> >=<img src="https://static.igem.org/mediawiki/igem.org/1/16/Xmusoftware_protein27.jpg">  and <img src="https://static.igem.org/mediawiki/igem.org/7/78/Xmusoftware_protein28.jpg"> ><img src="https://static.igem.org/mediawiki/igem.org/f/ff/Xmusoftware_protein29.jpg">  , sequence 1 dominates sequence 2.</div>

<div class="body_text">• If  <img src="https://static.igem.org/mediawiki/igem.org/f/fb/Xmusoftware_protein26.jpg"><<img src="https://static.igem.org/mediawiki/igem.org/1/16/Xmusoftware_protein27.jpg">  and <img src="https://static.igem.org/mediawiki/igem.org/7/78/Xmusoftware_protein28.jpg"> <=<img src="https://static.igem.org/mediawiki/igem.org/f/ff/Xmusoftware_protein29.jpg"> , sequence 2 dominates sequence 1.</div>

<div class="body_text">• If  <img src="https://static.igem.org/mediawiki/igem.org/f/fb/Xmusoftware_protein26.jpg"> <=<img src="https://static.igem.org/mediawiki/igem.org/1/16/Xmusoftware_protein27.jpg"> and <img src="https://static.igem.org/mediawiki/igem.org/7/78/Xmusoftware_protein28.jpg"> <<img src="https://static.igem.org/mediawiki/igem.org/f/ff/Xmusoftware_protein29.jpg"> , sequence 2 dominates sequence 1.</div>

<div class="body_text">1. Shortening the computing time of the software</div>

<div class="body_text">2. Expanding the range of the host cells</div>

<div class="body_text">3. Improving bacterium’s resistance to toxic molecule</div>

<div class="body_text">4. Advancing existed paths of synthetic biology by the method</div>

<div class="body_text">5. Designing new paths of synthetic biology by the method</div>

<div class="body_text">6. Increasing the output of recombinant protein</div>

<div class="body_text">7. Predicting the expression of heterologous gene in a new host cell</div>

</br>

<div class="body_heading">References</div>

<div class="body_references">1. Grantham, R.; Gautier, C.; Gouy, M.; Mercier, R.; Pave, A., Codon catalog usage and the genome hypothesis. <i>Nucleic acids research  </i>1980, <i>8 (1), </i> 197-197.</div>

<div class="body_references">2. Hershberg, R.; Petrov, D. A., Selection on codon bias. <i> Annual review of genetics </i> 2008, <i> 42,  </i>287-299.</div>

<div class="body_references">3. Gouy, M.; Gautier, C., Codon usage in bacteria: correlation with gene expressivity. <i> Nucleic acids research </i> 1982,  <i>10 (22), </i> 7055-7074.</div>

<div class="body_references">4. Fuglsang, A., Codon optimizer: a freeware tool for codon optimization.  <i>Protein expression and purification  </i>2003, <i>31 (2), </i> 247-249.</div>

<div class="body_references">5. Villalobos, A.; Ness, J. E.; Gustafsson, C.; Minshull, J.; Govindarajan, S., Gene Designer: a synthetic biology tool for constructing artificial DNA segments. <i> Bmc Bioinformatics </i> 2006,  <i>7 (1), </i> 285.</div>

<div class="body_references">6. Puigbò, P.; Guzmán, E.; Romeu, A.; Garcia-Vallvé, S., OPTIMIZER: a web server for optimizing the codon usage of DNA sequences.  <i>Nucleic acids research </i> 2007, <i> 35 (suppl 2), </i> W126-W131.</div>

<div class="body_references">7. Smith, D.; Yarus, M., tRNA-tRNA interactions within cellular ribosomes.  <i>Proceedings of the National Academy of Sciences  </i>1989,  <i>86 (12), </i> 4397-4401.</div>

<div class="body_references">8. Coleman, J. R.; Papamichail, D.; Skiena, S.; Futcher, B.; Wimmer, E.; Mueller, S., Virus attenuation by genome-scale changes in codon pair bias. <i>Science </i> 2008, <i> 320 (5884), </i> 1784-1787.</div>

<div class="body_references">9. (a) Wildt, S.; Gerngross, T. U., The humanization of N-glycosylation pathways in yeast. Nature Reviews Microbiology 2005, 3 (2), 119-128; (b) Morello, E.; Bermudez-Humaran, L.; Llull, D.; Sole, V.; Miraglio, N.; Langella, P.; Poquet, I., Lactococcus lactis, an efficient cell factory for recombinant protein production and secretion.  <i>Journal of molecular microbiology and biotechnology  </i>2007,  <i>14 (1-3), </i> 48-58.</div>

<div class="body_references">11. Chung, B.; Lee, D.-Y., Computational codon optimization of synthetic gene for protein expression. <i> BMC systems biology </i> 2012, <i>6 (1), </i> 134.</div>

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<div class="safety_s1h2">1. List and describe  all new or modified coding regions you will be using in your project. (If you use parts from the 2013 iGEM Distribution without modifying them, you do not need to list those parts.)</div>

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<table border="1" cellspacing="0">

<tr>

<td></td>

<td>Part number. </td>

<td>Where did you get the physical DNA for this part (which lab, synthesis company, etc) </td>

<td>What species does this part originally come from?</td>

<td>What is the Risk Group of the species?</td>

<td>What is the function of this part,in its parent species ?</td>

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<td> BBa_C0040</td>

<td>Synthesized, Blue Heron </td>

<td>Acinetobacter baumannii </td>

<td>2</td>

<td>Confers tetracycline resistance </td>

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<td>1</td>

<td>BBa_K1070000</td>

<td>PCR, Dr.Baishan Fang's lab, Xiamen University</td>

<td>Registry of Standard Biological Parts</td>

<td>1</td>

<td>The promoter induced by arabinose</td>

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<td>2</td>

<td>BBa_K1070001</td>

<td>PCR, Dr.Baishan Fang's lab, Xiamen University</td>

<td>Registry of Standard Biological Parts</td>

<td>1</td>

<td>The promoter induced by arabinose</td>

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<td>3</td>

<td>BBa_K1070002</td>

<td>PCR, Dr.Baishan Fang's lab, Xiamen University</td>

<td>Registry of Standard Biological Parts</td>

<td>1</td>

<td>The promoter induced by arabinose</td>

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