Team:UPO-Sevilla/Project/Basic Flip Flop/Modeling/Other Models

From 2011.igem.org

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<code>
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<textarea name="textarea" cols="30" rows="25" wrap="VIRTUAL">
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<p>for t=1:(tsim-1)</p>
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for t=1:(tsim-1)
      
      
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    Km_1=Km1*(1+Rep2(t)/Kiv)^beta;
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    Km_2=Km2*(1+r1(t)/Kiu)^gamma;
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    mRNA1(t+1)=abs(mRNA1(t)+tau*(Lambda1*RNAp/(Km_1+RNAp)-delta1*mRNA1(t)));
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    mRNA2(t+1)=abs(mRNA2(t)+tau*(Lambda2*RNAp/(Km_2+RNAp)-delta2*mRNA2(t)));
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    Km_1=Km1*(1+Rep2(t)/Kiv)^beta;<br>
 
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    Km_2=Km2*(1+r1(t)/Kiu)^gamma;<br>
 
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<br>
 
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    mRNA1(t+1)=abs(mRNA1(t)+tau*(Lambda1*RNAp/(Km_1+RNAp)-delta1*mRNA1(t)));<br>
 
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    mRNA2(t+1)=abs(mRNA2(t)+tau*(Lambda2*RNAp/(Km_2+RNAp)-delta2*mRNA2(t)));<br>
 
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<br>
 
     trigger
     trigger
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<br>   
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     k1=Kc1*mRNA1(t+1,1);<br>
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     k1=Kc1*mRNA1(t+1,1);
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     k2=Kc2*mRNA2(t+1,1);<br>
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     k2=Kc2*mRNA2(t+1,1);
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     Rep1(t+1)=abs(Rep1(t)+tau*(k1*Rib/(Kmu+Rib)-d1*Rep1(t)));<br>
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     Rep1(t+1)=abs(Rep1(t)+tau*(k1*Rib/(Kmu+Rib)-d1*Rep1(t)));
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     Rep2(t+1)=abs(Rep2(t)+tau*(k2*Rib/(Kmv+Rib)-(d2+ktemp)*Rep2(t)));<br>
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     Rep2(t+1)=abs(Rep2(t)+tau*(k2*Rib/(Kmv+Rib)-(d2+ktemp)*Rep2(t)));
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    r1(t+1)=(Rep1(t+1)/K)/((1+IPTG/Ki)^eta);
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    r1(t+1)=(Rep1(t+1)/K)/((1+IPTG/Ki)^eta);<br>
 
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<br>
 
end
end
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</code>
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</textarea>
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<p>The ‘trigger’ is just the moments where we simulate the effect of adding IPTG or increasing temperature. Even, the parameters are the same as the last Simbiology simulation.</p>
<p>The ‘trigger’ is just the moments where we simulate the effect of adding IPTG or increasing temperature. Even, the parameters are the same as the last Simbiology simulation.</p>

Revision as of 17:50, 20 September 2011

Grey iGEM Logo UPO icon

Other Models

Now, we show a different vision of the model presented. All of them are implemented in Matlab’s scripts or using the Simulink tool.

Basic Toggle switch general Equation Model

Here we have integrated the equations using the Euler approximation method. In spite of the overall behavior may change, the results obtained show a similar approximation to the previous graphics.

We are using this new method to show a different integration method than the used before. Furthermore, here we use a discrete approximation facing the previous. It is easier to show how the repression acts as modifier of the Michaelis constant of transcription process.

The algorithm used evaluates each species and its change in every step of the algorithm. So the algorithm is:

The ‘trigger’ is just the moments where we simulate the effect of adding IPTG or increasing temperature. Even, the parameters are the same as the last Simbiology simulation.

If we run this program, it offers the following result:

Translation 1 Rate

Basic Toggle Switch General Equation Model with Simulink

Here we present another implementation on Simulink, using Matlab again.

In the model we have included the overall process of the transcription and translation as a first-order kinetic model. The only exception to that vision is that the repression is treated as a modifier of the Michaelis constant of the transcription.

The diagram of the process is the following:

Translation 1 Rate

These results show no relevant information with respect to the models already presented. But the way to implement the system is closer to the vision of systems theory. Under this environment, we show all processes as first order systems.

Basic Toggle Switch Simplified Equations Model

Now we show an overall vision of the general behavior of the system. It is based on the simplification made by Timothy S. Gardner. It only includes the changes on the repressors concentration. This is:

Repressor1:

Translation 1 Rate

Repressor2:

Translation 1 Rate

And including the effects of IPTG or temperature:

Repressor1:

Translation 1 Rate

Repressor2:

Translation 1 Rate

These simple equations offer an external view of the system (a black-box model), and not in the internal processes in the bacteria analyzing the input-output relations.

The parameters α1 or α2 just show the force of each repressor of the proteins’ expression, while β and γ model the cooperativity of the mutual inhibition. If we simulate this scheme:

Translation 1 Rate

Although the behavior is not the same than the one showed before, due to the fact that there exists a delay at the moment of the switch, it is a good approximation for a qualitative analysis.

In other script, we’ve tried to show a different integration.

Translation 1 Rate

In the following image, we are showing the concentration of one repressor against the other.

Translation 1 Rate

At first, the repressor2 wins the battle, but it’s just casual, and in a different simulation may occur this:

Translation 1 Rate