Team:Grenoble/Modeling/Signaling

From 2012.igem.org

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K' = 9*10<span class="exposant">-8</span>mol.L<span class="exposant">-1</span></td>
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<td class="column1">Hill Coefficient</td>
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<td class="column2">n = 2</td>
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<td class="column3">We took a number greater than one to indicate positive cooperativity.</td>
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Revision as of 03:18, 25 September 2012

iGEM Grenoble 2012

iGEM Grenoble iGEM
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Project

Overview



The design of signaling module is given by the figure below:



Once the dipeptide molecule is fixed to the TAP receptor, it activates the phosphorylation of OmpR. OmpR* (the phosphorylated form of OmpR) is the transcription factor that activates the gene expression of cya.
This approach arises a few questions we cannot answer without computer models:

1- How sensitive is the signaling module? And what dipeptide concentration should be present in order to trigger a response?

2- How long does it take for the dipeptide to trigger a response?

Answering these two questions will help us assess the sensitivity and the rapidity of the whole system (ie the signaling module, the amplification module and the quorum sensing).

To answer these questions, we need to set the ordinary equations that govern AC evolution. Thus we can plot the evolution of AC concentration versus initial dipeptide concentration (sensitivity) as well as the temporal evolution of AC concentration (rapidity).

ODEs

Let’s begin by considering the cya gene activation by the transcription factor OmpR*.
As it is a gene activation, the transcription rate is usually modelized by a hill function:



Where Vm is the maximal transcription rate, k is the activation coefficient, p is the basal production coefficient and α the degradation coefficient.
For OmpR phosphorylation, we considered in the literature a model that takes into account the enzymatic mechanism of the Histidine Kinase EnvZ as well as the phosphotransfer and the phosphatase.
The model we use is a phenomenological extension of the Goldbetegivenr-Koshland biochemical switch model.[1]
The resulting equation governs [OmpR*] temporal evolution (the square brackets denote concentration) and highlights the fact that the phosphorylation is activated by dipeptide.



where K and K’ are the dimensionless Michaelis-Menten coefficients.
Since the process involved in the production of the new protein AC proceed at much slower timescale than the phosphorylation process that aims at chemically modifying the existing protein OmpR, time derivative of [OmpR*] is null (click here for more explanation) and [OmpR] and [OmpR*] are linked by a conservation law:
[OmpR*]+[OmpR]=[OmpR]tot

Once we have set the derivative equation of OmpR* equals to zero and replaced the value of [OmpR] and [OmpR*] by their expressions involving the total quantity of OmpR, we get a second order polynomial equation of [OmpR*]:



If we define the coefficients



We notice that the coefficient “a” is always negative because the dephosphorylation rate of OmpR* is lower in value than the phosphorylation rate .As a product of kinetic parameters, the coefficient “c” is positive.
For the reasons given above the determinant is positive:

Δ=b²-4 a c>0


We have then two float roots x and y. Their product is given by x.y = c/a. As “a” and “c” have opposite signs, x and y have opposite signs: We choose the positive root.

Parameters



Here is the link to the parameters of the amplification module we sometimes refer to.

Constants Value Derivation
Total quantity [OmprR]tot 6.8*10-8mol.L-1 The average number of OmpR molecules per cell is 80.769 ± 0.719 [2]. Knowing the cell volume (vc = 1.1*10-15L[3]) and the Avogadro number NA = 6.02*10-23mol.L-1, we deduce [OmpR]tot = 80/(NA*vc) = 6.8*10-8mol.L-1
Goldbeter-Koshland model constants v = 80 L-1.min-1
V' = 7*10-8mol.L-1.min-1
K = 7*10-7mol.L-1
K' = 9*10-8mol.L-1
Hill Coefficient n = 2 We took a number greater than one to indicate positive cooperativity.


References



  • [1] Alejandra C.Ventura, Jacques-A. Sepulchre, Sofia D.Merajver.
    A Hidden Feedback in Signaling Cascades Is Revealed. PLOS Computational Biology, 2008, 4, 3, e1000041.
  • [4] Michael D.Manson, Volker BlanK and Gabriele Brade.
    Peptide chemotaxis in E.Coli involves the Tap signal transducer and the dipeptide permease.Nature,15 May 1986,321,253-256.
  • [5] Edith Gstrein-Reider and Manfred Schweiger, Institut fur Biochemie (nat. Fak.),UniversitAt Innsbruck, A-6020 Innsbruck, Austria.
    Regulation of adenylate cyclase in E. coli.

Sensitivity

First of all, we plotted the evolution of the ratio ( [OmpR])/( [OmpR]tot ) versus the initial concentration of dipeptide to check if the phenomenological extension of Goldbeter-Koshland model is convenient and gives us the expected results.



We notice that the receptor has a maximal response at an initial dipeptide concentration of 10-6 Mol/L . This concentration represents the expected concentration of dipeptide out of the cell [4]. Thus we can say that the phosphorylation cascade is efficient as it enables as to have a maximal response for the expected conditions of use.

Next we need to plot the evolution of [AC] versus initial dipeptide concentration to assess sensitivity.



The sensitivity of the receptor is 10-8 Mol.L-1 of initial dipeptide concentration. However, at this level we are not able to assess the whole system sensitivity. We have got to wait for the amplification modeling results and link the two models to get an answer to the sensitivity question.

Click here to download the commented matlab code that gave us the figures above.

Time response

In order to have an idea of the rapidity of the detection, we plotted the temporal evolution of AC for two different initial dipeptide concentrations: 108 Mol/L and 10-6 Mol/L.







As we expected, we notice that we have the expected steady state values (see the graph above): 0.55 10-7 Mol.L-1 for an initial dipeptide concentration of 10-8 Mol/L and 1.1 10-7 Mol/L for an initial dipeptide concentration of 10-6 Mol/L .

Moreover, we see that to reach an AC production of 10-7 Mol/L we need approximately 600 mn. Click here to download the commented matlab code that gave us the temporal evolution.

Conclusion

Now we have an idea of the sensitivity and the rapidity of the receptor. However, to make sense, these values should be linked to the modeling results concerning the amplification loop.

But, Above all we are waiting for biological results to check the estimated parameters values and validate them.

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