The evolutionary fate of microbial communities can be difficult to predict. Even simple systems consisting of only a few species or strains can abruptly shift course, e.g. cooperating bacteria can gain mutations that cause them to compete.
Experiments have started to probe the evolutionary trajectories of simple microbial communities but the findings are often idiosyncratic—either due to differences in the specific setup or the fact that evolution relies on chance events. We lack general understanding and, importantly, null models that shape our expectations and allow for predictions.
This project addresses these significant gaps in our understanding by creating a framework that harnesses the availability of genome-scale metabolic networks to determine the distribution of evolutionary trajectories of coevolving microbes.
The postdoc will get the opportunity to use a database of metabolic models that the PIs have established, to identify broad statistical patterns concerning the nature of putative ecological interactions. From this point, the postdoc will then simulate the effects of mutations, growth, and reassortment to uncover simple predictive rules.
The project draws upon the strengths of our team and outlines a feasible set of scientific explorations that would constitute a successful postdoc. We also anticipate the postdoc’s studies to open up many additional avenues of exploration that will result in future collaborative ventures both within the team and with IceLab and the broader academic community.
This postdoc will be placed in IceLab, hosted by the Department of Mathematics and Mathematical Statistics and supervised by a multidisciplinary team with complementing expertise in modelling, network analyses, microbial ecology, and evolution prediction.