We are broadly interested in evolution, ecology, and population genetics with a focus on rapidly evolving pathogens such as HIV, influenza virus, or pathogenic bacteria. These organisms have large population sizes, short generation times and are subject to strong selective pressure for example by immune responses or drug treatment. Understanding their evolutionary dynamics and adaptability is important to limit the spread of these pathogens but also allows us to study fundamental questions in evolutionary biology and ecology. Please browse the pages linked below to find out more about what we are doing.
Within each infected person, HIV is in a continuous battle with the immune system, but without treatment, the immune system looses this fight almost always. The rapid evolution of HIV and the availability of samples stored in biobanks makes it possible to observe evolution from one year to another. We performed deep sequencing of longitudinal samples from multiple patients and thereby obtained a high resolution movie of HIV intrapatient evolution.
Seasonal influenza viruses evade human immunity through rapid evolution of their surface proteins. As a consequence, they can repeatedly infect the same individual and are maintained in the human population in recurring epidemics. Furthermore, vaccines against seasonal influenza strains require frequent updated to match the currently circulating viruses. Based on our theoretical understanding of rapidly adapting populations, we have developed methods to predict which of the circulating influenza viruses is most likely to take over and dominate the future population. We further developed tools for real-time tracking of influenza evolution.
The evolution of bacteria is infinitely more complicated than that of RNA viruses. In addition to mutations, bacteria can take up bits and pieces of genetic material from the environment. Even closely related bacteria often differ in the genes they contain. This horizontal transfer allows bacteria to adapt rapidly to new environments and accelerates the spread of antibiotic resistance. We are developing tools to analyse the evolution of bacterial genomes and study the evolution of resistance.
Classical population genetics focuses on neutral models with positive selection acting on a small number of loci. We are becoming increasingly aware that much of the standing variation we observe is under selection in some way or another, in particular in microbial populations. We study models with many loci under selection and derive predictions for genetic diversity that can be tested in sequence data.
In models with many loci under selection, closed analytic solutions are rarely available. To explore the accuracy of approximations or simulate scenarios with realistic parameters, forward simulations have become an essential tool. We have developed algorithms that allow for fast and flexible simulation of large populations.