Team:Evry/Model
Modeling
In silico experiments can help us improve the basic design of our system. We have performed an extended pathway analysis and we have studied possible feedback regulation systems, bringing a new approach on genetic circuitry to iGEM community.
The extension of pathway analysis consisted on Flux Balance Analysis (FBA). FBA help us understand how metabolites flux through the organism intracellular pathways. By studying these fluxes, we can predict the effect of genetic modifications - genes knock out or overexpressions - or the variations that initial carbon source can bring and optimize the system. We have modified a published SBML model of P. putida KT2440 to include our PLA production reactions and, by using OptFlux software, we have performed the analysis shown in Flux Balance Analysis.
In nature there are biological feedback processes that provide dynamic regulation. Organisms have control systems that allow activation or inactivation of genes, or protein translation, just in particular conditions. It also brings metabolic optimization, as proteins are expressed only when required through regulation. We have conceived a feedback system depending on a lactate biosensor and inhibition of promoters, as explained in Dynamic Regulation. In order to model it, we have applied two different approaches: rule based modeling based on Kappa language (Kappa model) and equations implemented on a genetic circuit (Circuit).
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