Speaker
Description
Microbes live in complex communities, where they continuously engage in a range of interactions with other microbes. One class of interactions is the exchange of metabolites, where one microbe synthesises a compound that is taken up by a neighboring microbe. This exchange allows some microbes to be non-producers for essential metabolites, instead relying on the biosynthetic activities of neighbors to obtain nutrients. The metabolic functions of a microbial community can therefore be distributed across individual microbes, leading to a collective metabolism. While it is known that microbial communities from many natural environments exhibit distributed metabolism, the structure and features of these distributed metabolic networks are not well understood. To gain insights into the relationships between microbes in a community, we describe an in silico approach to study how bacteria in a community interact via amino acid exchange, which represents one aspect of a community’s collective metabolism. Our approach uses existing genomic data from natural communities across various environments to construct genome scale models. Based on these models, we will predict how amino acid biosynthetic potential is distributed across communities. Using this data, we aim to understand how environmental variables shape distributed metabolism, and identify any universal features of distributed metabolism that appear across environments.
