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Team:USTC-Software/Project/Examples
From 2013.igem.org
Examples
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.
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).
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.
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:
Gene |
Avg |
S.D. |
p value |
Fold |
Compare |
||
IH100 |
IH105 |
IH100 |
IH105 |
||||
glnA |
2.91E-03 |
9.39E-04 |
6.80E-04 |
1.33E-04 |
1.30E-03 |
-3.1 |
fit |
ilvA |
5.06E-04 |
3.42E-04 |
1.86E-05 |
2.26E-05 |
3.00E-05 |
-1.48 |
unfit |
ilvE |
5.81E-04 |
3.58E-04 |
4.70E-05 |
5.77E-05 |
9.80E-04 |
-1.62 |
unfit |
ilvG |
1.97E-04 |
7.67E-05 |
2.65E-05 |
2.23E-05 |
4.40E-04 |
-2.57 |
unfit |
leuA |
6.99E-04 |
1.07E-03 |
9.21E-05 |
9.23E-05 |
1.30E-03 |
1.53 |
fit |
cobT |
1.00E-05 |
7.97E-05 |
7.82E-06 |
2.13E-05 |
8.50E-04 |
7.97 |
unfit |
cobU |
4.26E-05 |
1.22E-04 |
1.79E-05 |
1.95E-05 |
9.90E-04 |
2.85 |
unfit |
lacA |
5.14E-03 |
1.21E-03 |
1.54E-03 |
3.52E-04 |
2.50E-03 |
-4.24 |
unfit |
lacZ |
2.10E-03 |
5.14E-04 |
3.77E-04 |
1.34E-04 |
2.20E-04 |
-4.08 |
unfit |
lacY |
1.62E-03 |
4.08E-04 |
2.53E-04 |
7.95E-05 |
9.80E-05 |
-3.96 |
unfit |
ompF |
7.23E-03 |
2.35E-03 |
1.90E-03 |
3.69E-04 |
2.40E-03 |
-3.07 |
fit |
gltD |
9.91E-04 |
1.40E-04 |
1.88E-04 |
3.06E-05 |
1.10E-04 |
-7.1 |
fit |
lpdA |
1.07E-03 |
7.60E-04 |
1.17E-04 |
7.75E-05 |
4.60E-03 |
-1.41 |
fit |
rffT |
5.81E-06 |
3.65E-05 |
4.66E-06 |
2.86E-05 |
9.40E-04 |
6.28 |
fit |
ndh |
5.03E-05 |
1.46E-04 |
1.94E-05 |
3.29E-05 |
2.50E-03 |
2.9 |
fit |
cheR |
1.29E-04 |
2.68E-05 |
2.07E-04 |
1.75E-05 |
1.30E-03 |
-4.82 |
fit |
sodA |
3.80E-04 |
9.74E-04 |
1.06E-04 |
6.26E-05 |
7.00E-05 |
2.57 |
fit |
sodB |
7.80E-04 |
1.91E-03 |
2.45E-04 |
4.11E-04 |
3.30E-03 |
2.44 |
fit |
cpdB |
1.92E-05 |
7.56E-05 |
1.24E-05 |
1.40E-05 |
9.50E-04 |
3.94 |
fit |
guaA |
8.25E-04 |
4.31E-04 |
5.43E-05 |
1.34E204 |
1.60E203 |
-1.91 |
unfit |
yiaJ |
3.47E-05 |
6.15E-04 |
1.74E-05 |
1.64E204 |
4.10E204 |
17.74 |
fit |
dsdX |
1.05E-05 |
3.88E-05 |
5.23E-06 |
2.44E205 |
1.70E203 |
3.7 |
fit |
oppD |
2.32E-05 |
8.02E-05 |
1.81E-05 |
1.66E205 |
3.50E203 |
3.46 |
fit |
glnL |
2.41E-04 |
3.99E-05 |
4.81E-05 |
2.81E205 |
3.60E204 |
-6.04 |
fit |
oppA |
2.54E-03 |
5.06E-03 |
1.72E-04 |
5.68E204 |
1.40E204 |
2 |
fit |
oppB |
1.06E-04 |
3.57E-04 |
3.06E-05 |
6.22E205 |
3.60E204 |
3.35 |
fit |
proV |
2.50E-05 |
5.30E-05 |
7.34E-06 |
9.57E206 |
3.60E203 |
2.12 |
fit |
rbsC |
4.20E-05 |
1.12E-04 |
1.47E-05 |
2.70E205 |
3.90E203 |
2.67 |
fit |
hdeB |
1.09E-03 |
5.51E-06 |
1.80E-04 |
3.47E206 |
2.00E205 |
-198.5 |
fit |
yefM |
4.63E-04 |
8.12E-04 |
5.02E-05 |
6.07E205 |
1.10E204 |
1.75 |
fit |
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. 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%. Therefore, we may draw the following conclusion that our software could simulate the impact of new gene to some extent.
