Team:Stockholm/Modelling/Final eq
From 2010.igem.org
(Difference between revisions)
(3 intermediate revisions not shown) | |||
Line 4: | Line 4: | ||
|[[image:SU_modelling_Icon.gif|200px]] | |[[image:SU_modelling_Icon.gif|200px]] | ||
|width="10px"| | |width="10px"| | ||
- | |width="590px" | + | |width="590px" align="justify"| |
- | + | ||
=== A new gene and protein? === | === A new gene and protein? === | ||
After having the gene expressed in ''E. Coli'' the model that we came up with after studying extensively the articles [1]-[7] is as eq. 1 and eq. 2; where eq. 1 represents the dynamics for mRNA and eq. 2 represents the dynamics of protein translated from mRNA. These two equations are not complete though. | After having the gene expressed in ''E. Coli'' the model that we came up with after studying extensively the articles [1]-[7] is as eq. 1 and eq. 2; where eq. 1 represents the dynamics for mRNA and eq. 2 represents the dynamics of protein translated from mRNA. These two equations are not complete though. | ||
- | + | [[image:SU_final_equation.png|580px]] | |
- | + | ||
- | + | ||
- | |||
- | |||
In these equations there are variables that need to be defined and values found. The description to how to find the values are as below: | In these equations there are variables that need to be defined and values found. The description to how to find the values are as below: | ||
Line 22: | Line 18: | ||
*<VAR>γ<sub>M<sub>P</sub></sub></VAR> and <VAR>α<sub>M<sub>P</sub></sub></VAR>: for this case our approach is same as Yildirim et. al. 2003. We solve a system of equations for A = 0 and A <VAR>→∞</VAR>, in which when A = 0 we have no induction, and A <VAR>→∞</VAR> means high induction. At A <VAR>→∞</VAR>, we can assume that ,depending on the bacteria growth factor, mRNA production is increased at least 500folds (This is very dependent on the number of plasmids inside bacteria, environment and growth factor). So from these information one can calculate <VAR>γ<sub>M<sub>P</sub></sub></VAR> and <VAR>α<sub>M<sub>P</sub></sub></VAR>. | *<VAR>γ<sub>M<sub>P</sub></sub></VAR> and <VAR>α<sub>M<sub>P</sub></sub></VAR>: for this case our approach is same as Yildirim et. al. 2003. We solve a system of equations for A = 0 and A <VAR>→∞</VAR>, in which when A = 0 we have no induction, and A <VAR>→∞</VAR> means high induction. At A <VAR>→∞</VAR>, we can assume that ,depending on the bacteria growth factor, mRNA production is increased at least 500folds (This is very dependent on the number of plasmids inside bacteria, environment and growth factor). So from these information one can calculate <VAR>γ<sub>M<sub>P</sub></sub></VAR> and <VAR>α<sub>M<sub>P</sub></sub></VAR>. | ||
*<VAR>γ<sub>P</sub></VAR> and <VAR>α<sub>P</sub></VAR>: Unfortunately we couldn't find a reliable source for making scientific guess about degradation and production rate. However, one can from put forward reasonable values for these variables based on number of active ribosomes, growth factor, environment and translation elongation rate. These can be found in Bremer H. et al 1996. | *<VAR>γ<sub>P</sub></VAR> and <VAR>α<sub>P</sub></VAR>: Unfortunately we couldn't find a reliable source for making scientific guess about degradation and production rate. However, one can from put forward reasonable values for these variables based on number of active ribosomes, growth factor, environment and translation elongation rate. These can be found in Bremer H. et al 1996. | ||
+ | |||
+ | So as a final model, we propose equations 1 and 2, and equations 7.1 to 7.4e from "Proposing the model" section as the toy model for protein production prediction for our genes. | ||
+ | |} | ||
{{Stockholm/Footer}} | {{Stockholm/Footer}} |
Latest revision as of 19:42, 27 October 2010