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Model construction
Introduction
A FAIRE
We put a lot of efforts to use as much bibliographic data as possible to build this model. Then, we normalized our concentrations, so as to have
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]) :
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:
Conclusion
We finally obtained the following equations :
and the following parameters :
| Parameter Table
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| Parameter
| 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
| ¤
| ∅
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| θenvZ
| Hill characteristic concentration
|
| 0.5
| c.u
| ∅
|
| βenvZ
| Maximum production rate
|
| 1
| min-1
| ∅
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Now you have had a good overlook of our model, go see a more detailed justification!
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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.
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