Computational modeling of metabolism in microbial communities on a genome-scale

Abstract

Computational modeling of microbial communities using GEnome-scale Models (GEMs) of metabolism is a new frontier in systems biology. Here, we discuss recent developments in this area ranging from high-throughput GEMs reconstruction pipelines to approaches for modeling under steady-state and for simulating temporal, evolutionary, and spatiotemporal dynamics of microbial communities. We categorize these approaches based on flux balance analysis or elementary mode analysis of mixed-bag and compartmentalized GEMs and discuss their scope of applications and scalability for large-scale simulations. In addition, we review computational tools using GEMs for the design of microbial communities and recent efforts to integrate GEMs and machine learning for predicting interspecies interactions. We conclude by discussing best practices for using these tools and potential avenues for future development.