Team:Paris/Network analysis and design/Core system/Model construction/Detailed justification

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= Sum effect and linear modelling =
= Sum effect and linear modelling =
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* Yet, the flagella gene network has been thoroughly studied in [[Team:Paris/Network_analysis_and_design/Core_system/Model_construction#Bibliography|[1]]]. We used two major results presented in this study. Firstly, Shiraz Kalir and Uri Alon came up with the fact that the promoters of class 2 genes, among which fliL, flgA and flhB, behaved like SUM-gate functions with flhDC and fliA inputs. Secondly, their experiments proved that these influences could be considered as linear. Thus the following model:
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* The flagella gene network has been thoroughly studied in [[Team:Paris/Network_analysis_and_design/Core_system/Model_construction#Bibliography|[1]]]. We used two major results presented in this study. Firstly, Shiraz Kalir and Uri Alon came up with the fact that the promoters of class 2 genes, among which fliL, flgA and flhB, behaved like SUM-gate functions with flhDC and fliA inputs. Secondly, their experiments proved that these influences could be considered as linear. Thus the following model:
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[[Image:Promoter_Activity.jpg|center]]
[[Image:Promoter_Activity.jpg|center]]

Revision as of 16:03, 26 October 2008

Detailed justification


We shall present here a more detailed presentation of the choice we made as far as our model is concerned

Contents

Sum effect and linear modelling

  • The flagella gene network has been thoroughly studied in [1]. We used two major results presented in this study. Firstly, Shiraz Kalir and Uri Alon came up with the fact that the promoters of class 2 genes, among which fliL, flgA and flhB, behaved like SUM-gate functions with flhDC and fliA inputs. Secondly, their experiments proved that these influences could be considered as linear. Thus the following model:


Promoter Activity.jpg


β and β’ represent the relative influence of flhDC and fliA respectively, the units of β and β’ being time-1.

  • Furthermore, they came up with numerical values of β and β’ for each gene, which fitted quite well to their experiments. We then decided that we could use those values as well in our model.

Use hill quand on ne sait pas

Normalization

pour les beta

We kept the β and β’ values found by S. Kalir and U. Alon, since they showed the relative influence of flhDC and fliA. To have the same order of magnitude between each specie, we normalized those parameters between 0 and 1 as following. We reasoned independently for each equation, wishing to set the equilibrium values of the concentration to 1 given input values of 1. This gave:

Beta Resize.jpg


Beta p Resize.jpg


  • In fact, if we take CFP for example:
CFP.jpg

The maximum of [CFP] is reached when [fliA] = 1 and [flhDC] = 1 ; when we solve with these condidtions, we obtain :

CFP Solve.jpg

Then setting the equilibrium value of [CFP] to 1 corresponds to setting

Beta Gamma resize.jpg
  • The analysis of fliA is different, but not the result:
FliA Analysis.jpg

With an input of flhDC equal to 1, the solution of the differential equation is:

FliA Solve.jpg

And the condition on the equilibrium imposes

Beta Gamma Resize FliA.jpg
  • To conclude, we see that we always get the same condition:
Final Resize.jpg
  • Finally, since we had imposed γ=1 we resulted with β+β'=1.

pour les hill

Parameters table

Bibliography