William Harcombe, University of Minnesota
Predicting eco-evolutionary dynamics is vital to our ability to understand and manage critical microbial communities. Using computational models and experimental systems we have found that metabolic mechanisms provide a basis for quantitatively predicting the dynamics of microbial systems. We have found that when microbes cross-feed, obtaining metabolites from one another, that the bacteria are more susceptible to antibiotic perturbation than they are when growing independently. Cross-feeding also changes the rate and mechanisms of adaptation to antibiotics. Inspired by the importance of metabolic mechanisms for driving eco-evolutionary dynamics we have helped develop a computational platform for quantitatively predicting the dynamics of microbial systems from genome-scale metabolic networks. An updated version of this platform, COMETS, has recently been released that enhances both the interface and the biological realism. Our work suggests that metabolic interactions between cells can have predictable impacts on eco-evolutionary dynamics in microbial communities.