DOI
10.1038/s41467-017-01407-5

Summary
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.
In this study the authors address this question by integrating genome-scale models of metabolism with evolutionary game theory. They used microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games. Over 80,000 in silico experiments were performed 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.

Relevance for Complex Systems Knowledge
This study provides a genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.

Point of Strength
In this study it was demonstrated that thanks to abstract theoretical ecology models it is possible to reveal how intracellular molecular mechanisms lead to the rise of non-intuitive ecological interactions. The analysis presented spans over 80,000 in silico experiments, which is beyond the current experimental capabilities. This study provides testable predictions that can be used as a guideline for the design of future-targeted experiments built upon previously established synthetic communities