Team:Paris/Analysis/Construction

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Model construction


Contents

Introduction

A FAIRE We wished to build a model that could be used to help us design our biological system. We shall hereby describe the asumptions we made

bcp de données bibliographique + gros effort de normalisation dou détermination de tous les parametre et pas de pipo

Classical model and temporal rescaling

  • Classically we use the following equation to model gene interactions (see for example in [1]) :


Classical equation.jpg


where [Y] denotes the concentration of Y protein and γ its degradation rate (which unit is time-1).

  • We did a lot of bibliography work and found many interesting expressions found in S.Kalir and U. Alon article. We also kept the parameters values. When we did not find relevant information, we chose a classical Hill function.
  • We normalized every concentration, so that their value would range between 0 and 1. Indeed, we found it necessary because we needed to be able to compare the respective influences of these concentrations.
  • Furthermore, it is important to note that this degradation rate represents both the influence of the degradation and dilution. We assume that the degradation can be neglected compared to the dilution caused by the cell growth. Thus, every degradation rates are equal. We kept the designation “degradation rate” for convenience, so as not to mix up with the dilution that might occur elsewhere.
  • We therefore wanted to have a proper time scale. We then set the degradation rates, γ ,to 1. Since we can know easily the value of the real half-time, we may know the real timescale out of our computations. Then we have:


Gamma Expression.jpg


Conclusion

We finally obtained the following equations :

Eqn flhDC.jpg
FliA dynamics.jpg
CFP.jpg
YFP.jpg
Eqn EnvZ-RFP.jpg

and the following parameters :

Parameter Table
Parameter Parameter in code Meaning Original Value Normalized Value Unit Source


γ Degradation rate 0.0198 1 min-1 wet-lab
βFlhDC Maximum production rate 1 min-1
βFliA FlhDC activation coefficient 50 0.1429 min-1 [1]
β'FliA FliA activation coefficient 300 0.8571 min-1 [1]
βCFP FlhDC activation coefficient 1200 0.8276 min-1 [1]
β'CFP FliA activation coefficient 250 0.1724 min-1 [1]
βYFP FlhDC activation coefficient 150 0.3333 min-1 [1]
β'YFP FliA activation coefficient 300 0.6667 min-1 [1]
βRFP FlhDC activation coefficient 100 0.2222 min-1 [1]
β'RFP FliA activation coefficient 350 0.7778 min-1 [1]
nenvZ Hill coefficient 4 ¤
θenvZ Hill characteristic concentration 0.5 c.u
βenvZ Maximum production rate 1 min-1

Now you have had a good overlook of our model, go see a more detailed justification!

Liens

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Have a look at our detailed justification! Have a look at our Akaike criteria!

Bibliography

  • [1] Shiraz Kalir, Uri Alon. Using quantitative blueprint to reprogram the dynamics of the flagella network. Cell, June 11, 2004, Vol.117, 713-720.