Modeling microbial communities with evolutionary game theory
Within the past decade we have observed a substantial research effort towards a deeper characterization of the human microbiome and its role during both physiological and pathological conditions as well as understanding their impact on the host’s organism . Specific importance in health and disease has been assigned to gut  and lung  microbiota. Since modeling such complex systems of interactions with many hundreds of different species seems computationally infeasible, various scientists focused on identifying model microbial communities . Based on the available literature most of them comprise just a few key agents.
We had previously developed a Python package dedicated to simulating population dynamics according to the rules of evolutionary game theory . In this simple framework we analyze the growth of five various bacterial species, some of which present an antagonistic effect (such as bacteriocins) towards others selected. We show that based on very basic assumptions it is possible to trace the expansion of distinct subpopulations and identify plausible causal interactions leading to the observed dynamics. The following study could serve as a preliminary towards further, more complex simulations of microbiome growth.
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