TY - JOUR T1 - Bridging traditional evolutionary game theory and metabolic models for predicting Nash equilibrium of microbial metabolic interactions JF - bioRxiv DO - 10.1101/623173 SP - 623173 AU - Jingyi Cai AU - Tianwei Tan AU - Siu Hung Joshua Chan Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/05/14/623173.abstract N2 - Inter-cellular interactions are ubiquitous in the world of microbes, shaping the population composition of ecosystems at both microscopic and macroscopic scales, affecting human health and governing processes in utilization of bio-resources. However, metabolite exchanges, a major type of microbial interactions, remain difficult to measure and predict, invoking the urgent need of modeling and computational studies. As an alternative to the conventional ecological models which usually lack metabolic details, metabolic models and flux-balance-analysis (FBA) based algorithms emerge as a promising way to address the challenge. However, existing algorithms for predicting microbial community metabolism usually impose constraints or objective functions (implicitly or explicitly) that lead to ‘forced altruism’, which forces a microbe to fulfill other species’s need by cross feeding certain metabolites instead of using the resource for its own reproduction and other cellular activities in order to achieve community level optimality. As a result, in terms of game theory, the prediction is not necessarily a Nash equilibrium and therefore not evolutionarily stable. We developed a bi-level optimization framework free of ‘forced altruism’ constraints termed NECom. Payoff matrices of metabolic strategies analogous to traditional matrix games can be obtained by shadow price analysis in FBA to validate NECom predictions. By applying NEcom to toy community models, we demonstrate several classical games between microbes in terms of metabolic interactions, including prisoner’s dilemma and positive frequency-dependent cooperation. The results provide insights into why microbes may not prefer cooperation even if it is mutual beneficial, and why sometimes mutualism is still favorable when the resource investment seemingly contradicts to a microbe’s fitness, demonstrating NECom a promising tool to reveal metabolic mechanisms of microbial interactions. The novel tools reported in this paper bridge traditional evolutionary game theory and metabolic models for better analysis of microbial metabolic interaction. ER -