Grant: Discovery Grants Program - Individual

Genomic data for bacteria are now being gathered in large quantities and serially through time. While large genomic studies have been primarily descriptive in nature, these data present the opportunity to build and test quantitative models in evolution and ecology, at a high level of resolution, for the first time. This is the long-term vision of this Discovery Grant research program: I plan to move genomic studies out of the description-only realm, developing a body of research in which genomic data can help to build quantitative descriptions of ecological phenomena and are used to refine and test hypotheses. In the next five years, I will focus on a key area of uncertainty, namely how bacteria structure their populations and create and maintain diversity. I aim to bridge the gap between models and genomic data, connecting phylogenomic data with modelling and statistics to understand the creation and maintenance of diversity in three distinct bacterial model systems. There are three central objectives over the next five years: (1) to identify population structure at a range of scales and define meaningful subgroups directly from phylogenomic data; (2) to characterize inter- and intra-group competition and quantify diversity, building interpretable models that match genomic data, and (3) to use the models to make short-term predictions and thereby test models with serially sampled data. I will use data from three bacterial systems, comprising both phylogenetic data (that evolves by descent along a phylogenetic tree) and "non tree-like" data (data that do not, including genes that are transferred horizontally, convergent loci, phenotypic data and metadata). These are used here as models of natural systems that are evolving on observable time scales in the presence of selection. Data for these systems are available in part due to relevance for human health, but the focus here is on fundamental phenomena. There are four key advantages to this approach: (1) the use of whole-genome sequence (WGS) data will enable us to work at different levels of resolution, from small fine-grained scales to broader populations; (2) we can detect and model both inertial and adaptive patterns, as we have information about selection; (3) we have both more and less "tree-like" data in these three distinct systems so we can complement phylogenetic methods with non-tree-like data; and (4) data are now being gathered over time, affording the opportunity to model ecological processes through time, fit models to phylogenomic data and test model predictions.