Speciation genomics, adaptation and gene transfer networks in bacteria

Supervisors: Tim Barraclough (Dept Life Sciences, Imperial College London, Silwood Park campus), Richard Everitt (Dept Mathematics and Statistics, Univ Reading).

Bacteria constitute a massively diverse radiation of life, but the mechanisms of diversification remain less well understood than in sexual eukaryotes. In particular, although all bacteria reproduce clonally, many engage in a wide variety of mechanisms of recombination that can transfer DNA between individuals and divergent species. We still lack detailed understanding of how recombination influences both the ability of bacteria to diverge into distinct species and the way that bacteria adapt to environmental change.

This project will use genomic data to infer networks of gene transfer in bacteria and to determine the consequences of transfer events for speciation and adaptation. Alternative models for the structure of recombination will be compared – for example, a model of gradually declining gene transfer with genetic distance versus a model of discrete species units defined by mechanisms preventing gene transfer between them. The results will be used to test recent models for alternative mechanisms of bacterial speciation. A key component will be to develop computational methods that are feasible for large datasets and flexible enough to encode alternative sets of evolutionary mechanisms.

Population genetic models will then be used to determine how the shape of gene transfer networks affects evolutionary responses to contemporary environmental change. For example, do multiple species tend to evolve independently and in parallel or do beneficial mutations arising in one species spread through a wider clade or community? Does adaptation involve sequential beneficial mutations in a clonal background or recombination of beneficial variants from different genomic backgrounds? Which mechanisms of gene transfer are most effective in responding to different kinds of environmental change? Results will be applied to antibiotic resistance in waste water communities in order to improve management actions for limiting the spread of environmental antibiotic resistance.

The project will involve computational modelling, Bayesian statistics, Monte Carlo methods, bioinformatics, evolutionary analysis and genomics. Depending on your interests, it may also include evolution experiments on speciation or whole community responses in the laboratory. We are keen to shape the exact focus and range of approaches to match the student’s own experience and interests.

The student would join the Centre for Doctoral Training in Quantitative and Modelling Skills in Ecology and Evolution (QMEE), https://www.imperial.ac.uk/qmee-cdt/, which provides training in the quantitative and modelling skills needed to address real-world problems by connecting theory, data, and practice. You could be a life sciences student interested in quantitative methods or a mathematics, computing or physical sciences student interested in applying your skills to evolutionary problems.

For more information about the project, contact Tim Barraclough at [Email Address Removed]. To apply, send your CV, a covering letter explaining why you are interested in the CDT and that project, and the names and e-mail addresses of two academic referees to [Email Address Removed]. At least one referee should have supervised you on a previous research project.