Difference between revisions of "Team:HUST-China/Sort of three genes"
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<p>If the gene expression parameter is lower than 1，we take the negative value of score. Then we sum all scores together, and get the final result.</p> | <p>If the gene expression parameter is lower than 1，we take the negative value of score. Then we sum all scores together, and get the final result.</p> | ||
<img src="https://static.igem.org/mediawiki/2018/archive/c/cf/20181014074548%21T--HUST-China--2018-sort-pic1.png" class="img-responsive" > | <img src="https://static.igem.org/mediawiki/2018/archive/c/cf/20181014074548%21T--HUST-China--2018-sort-pic1.png" class="img-responsive" > | ||
| − | <p>The curve: score(x,y) means the sum of the first x parameter conditions’ scores when the model runs for y units of times.</p> | + | <p>(The curve: score(x,y) means the sum of the first x parameter conditions’ scores when the model runs for y units of times.)</p> |
<p>The result shows that in more than 10，000 parameter conditions, mles is the most important gene. So we sort three genes by their final scores: mles, lldp, ldhA.</p> | <p>The result shows that in more than 10，000 parameter conditions, mles is the most important gene. So we sort three genes by their final scores: mles, lldp, ldhA.</p> | ||
</div> | </div> | ||
Revision as of 07:26, 17 October 2018
Sort of three genes
1. Overview
In Rhodopseudomonas, the sequence of genes in plasmid influences them expression ability. (Shou-Chen.2012) We have three genes to transfer. So we should find out which gene is the most important, and we sort three genes by their importance.
But then we meet a problem that there are not enough references about Rhodopseudomonas for us to obtain enough parameters for our simulation, so we run the simulation in a large range of parameters for many times and use the statistical result to decide how to sort the three genes
2. Function
(We skip some reactions in tricarboxylic acid cycle, and let Isocitrate come to Malate in one reaction.)
3. Parameters
There are not enough references for us to get exact parameters. So we assort parameters into several groups and change one group’s value each time. At the same time, we change the expression abilities of three genes to find which is the most important in this parameter situation.
| Income parameters | Metabolism parameters | Cross membrane parameters | Ks parameters | Gene expression |
|---|---|---|---|---|
| gPyruvate | k2LdhA | Vmax2 | Ks1 | Ks2 |
| gAcetyl-CoA | k1LdhA | Ks3A | [LdhA] | |
| Vmax1 | Ks3M | Ks5 | ||
| Vmax3 | Ks4 | |||
| Vmax4 | ||||
| Vmax5 |
4. Result
We generate each group ’s value from 〖10〗^(-5) to 〖10〗^5，and run the simulation. At the beginning, we simply sum the concentration of Lactate outside. But it is not fair for each parameter situation because in some cases the value of final result is much lower than others. So we use the SOFTMAX function to turn the concentration result into scores from 0 to 1. The function is shown below:
If the gene expression parameter is lower than 1，we take the negative value of score. Then we sum all scores together, and get the final result.
(The curve: score(x,y) means the sum of the first x parameter conditions’ scores when the model runs for y units of times.)
The result shows that in more than 10，000 parameter conditions, mles is the most important gene. So we sort three genes by their final scores: mles, lldp, ldhA.
