Development of a general and user-friendly modelling paradigm to simulate multi metabolic networks

The analysis of complex biological systems through computational models al- lows scientists to gain deeper insights into underlying behaviors and interac- tions. Numerous methods exist for modeling such systems, depending on the specific research goals and field of study. This thesis focuses on Flux Balance Analysis (FBA), a widely used computational technique for analyzing metabolic networks and predicting cellular growth and metabolic flux distribution. To en- hance the usability and efficiency of FBA within the GreatMOD framework—a general framework designed for the creation and simulation of computational models—we developed an automated and generalized system that simplifies the generation of C++ code for FBA simulations, making the process more accessible for users with limited programming experience. In addition to au- tomating FBA, the GreatMOD graphical user interface was updated to include a dedicated command for easy access to the FBA system. To validate its func- tionality and efficiency, we developed a simplified microbial interaction model of the human gut, known as the SIHUMx model, designed to simulate dynamic environments and metabolite exchanges between bacterial species, allowing us to assess the system’s ability to replicate complex metabolic behaviors. Further- more, the automation and generalization of the system improved computational efficiency, expanding the potential applications of the GreatMOD framework in systems biology