Team:NTNU Trondheim/Model


Revision as of 08:41, 7 August 2012 by Rolfheil (Talk | contribs)

Bacterial Anti-Cancer-Kamikaze




To get a better understanding of the dynamics of the system, we made a model of the system using the Cain software1 for stochastic simulations. While stochastic simulations are more computationally demanding than deterministic models based on solving ODE's, they allow for random fluctuations that may have a big impact on the system in a cell(2).

As we want our system to react to two different signals, three promoters were required. One to respond to lactate, one to respond to low oxygen and a third to respond to signal molecules controlled by the two other promoters. The last promoter would then control cell lysis. For lactate sensing, we have adapted the lld promoter of E. coli, the vgb promoter from Vitreoscilla is used for the oxygen while the Lux promoter from Vibrio fischeri were used for lysis control. See respective pages for more details.

The model can be divided into three parts; the lld promoter, the vgb promoter and the lux promoter. Each of these systems was first modelled separately. This made it easier to observe the effect of changing individual parameters and make reasonable estimates when experimental data were not available. The final model contained 66 reactions and 46 parameters, many of which were estimates. However, we experienced that working with the model and observing how parameter changes influenced protein expressions gave valuable insight into the system.

Stochastic models, as opposed to deterministic models, describe the presence of species with the number of molecules. For small molecules, in this case oxygen and lactate, this gives very high numbers. For example; a 1 mmol concentration of lactate gives about 400000 molecules in the cell assuming a cell volume of 0.7 mu m^3. This many molecules are computationally demanding to keep track of and the diffusion of these molecules through the cell membrane is relatively fast(3). To simplify, lower, constant consentrations and higher propensity functions were used. A possible problem with this simplification is greater fluctuations than a more realistic model.

In general, the models seems to be too sensitive with small stimulations giving very large effects. A possible reason is that the values we used for the translation rates are too high, as these are estimates. Another possibility is that the propensity for mRNA decay are more different between deterministic models, as in the references, and stochastic models than expected. This was especially the case for the Lux promoter.


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