Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities

Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.We gratefully acknowledge funding from the Defense Advanced Research Projects Agency (Purchase Request No. HR0011515303, Contract No. HR0011-15-C-0091), the U.S. Department of Energy (Grants DE-SC0004962 and DE-SC0012627), the NIH (Grants 5R01DE024468 and R01GM121950), the national Science Foundation (Grants 1457695 and NSFOCE-BSF 1635070), MURI Grant W911NF-12-1-0390, the Human Frontiers Science Program (grant RGP0020/2016), and the Boston University Interdisciplinary Biomedical Research Office ARC grant on Systems Biology Approaches to Microbiome Research. We also thank Dr Kirill Korolev and members of the Segre Lab for their invaluable feedback on this work. (HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; DE-SC0004962 - U.S. Department of Energy; DE-SC0012627 - U.S. Department of Energy; 5R01DE024468 - NIH; R01GM121950 - NIH; 1457695 - national Science Foundation; NSFOCE-BSF 1635070 - national Science Foundation; W911NF-12-1-0390 - MURI; RGP0020/2016 - Human Frontiers Science Program; Boston University Interdisciplinary Biomedical Research Office ARC)Published versio

The Host-Pathogen Game: An Evolutionary Approach to Biological Competitions

We introduce a model called Host-Pathogen game for studying biological competitions. Notably, we focus on the invasive dynamics of external agents, like bacteria, within a host organism. The former are mapped to a population of defectors that aim to spread in the extracellular medium of the host. In turn, the latter is composed of cells, mapped to a population of cooperators, that aim to kill pathogens. The cooperative behavior of cells is fundamental for the emergence of the living functions of the whole organism, since each one contributes to a specific set of tasks. So, broadly speaking, their contribution can be viewed as a form of energy. When bacteria are spatially close to a cell, the latter can use a fraction of its energy to remove them. On the other hand, when bacteria survive an attack, they absorb the received energy, becoming stronger and more resistant to further attacks. In addition, since bacteria play as defectors, their unique target is to increase their wealth, without supporting their own kind. As in many living organisms, the host temperature plays a relevant role in the host-pathogen equilibrium. For instance, in animals like human beings, a neural mechanism triggers the increasing of the body temperature in order to activate the immune system. Here, cooperators succeed once bacteria are completely removed while, in the opposite scenario, the host undergoes a deep invasive process, like a blood poisoning. Results of numerical simulations show that the dynamics of the proposed model allow to reach a variety of states. At a very high level of abstraction, some of these states seem to be similar to those that can be observed in some living systems. Therefore, to conclude, we deem that our model might be exploited for studying further biological phenomena

Metabolic Games

Metabolic networks have been used to successfully predict phenotypes based on optimization principles. However, a general framework that would extend to situations not governed by simple optimization, such as multispecies communities, is still lacking. Concepts from evolutionary game theory have been proposed to amend the situation. Alternative metabolic states can be seen as strategies in a “metabolic game,” and phenotypes can be predicted based on the equilibria of this game. In this survey, we review the literature on applying game theory to the study of metabolism, present the general idea of a metabolic game, and discuss open questions and future challenges