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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:

for t=1:(tsim-1)

Km_1=Km1*(1+Rep2(t)/Kiv)^beta;
Km_2=Km2*(1+r1(t)/Kiu)^gamma;

mRNA1(t+1)=abs(mRNA1(t)+tau*(Lambda1*RNAp/(Km_1+RNAp)-delta1*mRNA1(t)));
mRNA2(t+1)=abs(mRNA2(t)+tau*(Lambda2*RNAp/(Km_2+RNAp)-delta2*mRNA2(t)));

trigger
k1=Kc1*mRNA1(t+1,1);
k2=Kc2*mRNA2(t+1,1);
Rep1(t+1)=abs(Rep1(t)+tau*(k1*Rib/(Kmu+Rib)-d1*Rep1(t)));
Rep2(t+1)=abs(Rep2(t)+tau*(k2*Rib/(Kmv+Rib)-(d2+ktemp)*Rep2(t)));
r1(t+1)=(Rep1(t+1)/K)/((1+IPTG/Ki)^eta);

end