This action funds an NSF Postdoctoral Research Fellowship for FY 2009. The fellowship supports a research and training plan entitled "Inference of microbial population parameters from metagenomic data" for Philip Johnson. The host institution for this research is Emory University and the sponsoring scientist is Rustom Antia.
Despite a growing awareness of the ecological importance of non-pathogenic microbes, the forces affecting the evolution of natural microbial populations remain largely unknown. Recent technological advances in DNA sequencing have enabled metagenomic projects that sample and sequence DNA from natural environments to yield, in essence, a genome-wide population sample from the dominant species in the sample. This research develops a method for estimating the rate of exchange of genetic material among microbes (i.e. the recombination rate) from such data. In contrast to previous models, this new method incorporates empirical evidence that microbial recombination rates are negatively correlated with sequence divergence. Two parallel approaches are being taken: 1) a modified version of the standard coalescent process in which coalescent events are more likely between lineages that have recombined and 2) a two-stage procedure analogous to constructing an ancestral selection graph in which a large ancestral recombination graph is constructed, mutations are applied and then recombination events are accepted or rejected depending on sequence divergence.
Training goals include improving statistical and mathematical modeling expertise, specifically coalescent theory, importance sampling, and graph theory, while furthering interactions with experimental biologists. The inspiration for the proposed methods came from two metagenomic projects that sample microbial communities with serious environmental consequences: activated sludge in wastewater treatment plants and biofilms that grow in abandoned mines producing highly acidic runoff. Studying the evolution of these two populations will guide the design of engineering strategies to manage them. Outreach includes involvement of undergraduate students with data analysis, and the results of this research will be broadly disseminated through publication in peer-reviewed journals.
