Our research focuses on mathematical models describing the evolution and ecology of microbial genes and genomes.
We use mathematical and computational approaches to understand how the observed diversity of microbes emerged and how bacterial populations cooperate to adapt to their environment.
In population genetics, many theoretical results have been developed in times where not much genomic and genetic data were available. Although these theory-driven results are still essential, data-driven discoveries have meanwhile dramatically changed our view of evolution and ecology, in particular for prokaryotes. Today we are able to see low-frequency variations in genomic data, sequence the genomes of thousands of individuals, even at the level of a single cell, and track the occurrence of mutations and genes over time in experimental evolution.
We are working at the interface of these two worlds combining mathematical population genetics theory, computational biology, and machine learning.
For example, we introduced models that can explain the existence of huge gene reservoirs in bacterial populations (the pangenome) and analyzed the evolution of the CRISPR-Cas immune system against phages. More recently, we started to study how the cooperation of closely related bacterial strains affects genomic diversity and how machine learning methods can improve inference in bacterial population genetics.