Stochastic Analysis

Besides the parameter space search, we performed another type of analysis in order to verify that our system is robust and that it is not bistable. We were especially interested in the GFP band, whether it is always present and whether the amount of GFP produced in the cells for different concentrations of Acetaldehyde has large fluctuations. Because gene expression is an intrinsically stochastic process, we performed stochastic simulations to see how our system reacts to noise and how it responds to perturbations.

The following results show that our system is indeed robust and it does not have bifurcations.

Method

For the stochastic simulations we used the [http://magnet.systemsbiology.net/software/Dizzy/ Dizzy] software, to which we gave as an input an SBML file with the description of our system. Moving from deterministic to stochastic simulations we had to change several things in the SBML file. Firstly, we exported the SBML file to a CMDL format.Next, we converted the species values as well as the production rates and repression coefficients from concetrations (in μM) to numbers of molecules. Since our initial system was described in terms of ODEs, we also had to change this by separating the system into distinct reaction channels (i.e. separate degradation from activation/repression) and specifying the reaction rates.

With the new description of the system, we could start the simulations. For analyzing the robustness of the system, we chose 5 strategic Acetaldehyde concentrations, and for each of them we performed stochastic simulations. We selected the following Acetaldehyde concentrations:

• 0
• 25
• 50
• 100
• 300

Figure 1: GFP versus Acetaldehyde. The red squares denote the 5 Acetaldehyde concentrations for which we simulated the time course of GFP steady state in stochastic mode.

We started with a deterministic simulation to take the deterministic steady states of the species, after 5000 min(the acetaldehyde amount was the one that leads to the maximal GFP value, i.e the peak of the band). Starting from deterministic steady states, we performed a stochastic simulation with Gillespie-direct algorithm for 10000 min in order to get the stochastic steady states. After that, we restarted the stochastic simulation having the stochastic steady state values as our initial and simulated for 100 000 more minutes (storing the GFP values every minute). We collected all the GFP values from the last simulation and plotted them in a histogram (150 bins).

Simulations

Figure 4: Exploring the parameter space of TetR degradation rate

Figure 2: Exploring the parameter space of TetR degradation rate	Figure 3: Exploring the parameter space of CI degradation rate
Figure 5: Exploring the parameter space of CI degradation rate	Figure 6: Exploring the parameter space of TetR degradation rate
Figure 7: Exploring the parameter space of CI degradation rate We see in Figure 1 that we obtain a Gaussian distribution of GFP number of molecules at the steady state, with a mean of 23491 molecules and standard deviation of 667 molecules. If we convert this value to concentration we get 19.5046 μM, which is very close to what we obtained deterministically. We see that the system keeps fluctuating very closely to its steady state, with no large jumps away from it. The fact that the Gaussian distribution is unimodal tells us that our system is monostable. From this analysis we confirmed what we concluded before and that is the robustnes of our system, especially of the GFP band. We can now be sure that for a certain acetaldehyde concentration once the band appears, it won't start fading away.

Analysis of Results

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