Our research lies at the intersections of evolution with ecology and with medicine. Using mathematical models, we study which factors promote or hamper rapid adaptation to changing environments. Whether populations succeed in adapting to harsh environmental change or go extinct is one of the key questions of evolutionary biology. The answer is of relevance in very different contexts with opposite goals: in conservation biology (where we hope for adaptation and species persistence) and in medicine or agriculture (where we want to prevent successful adaptation, i.e. the evolution of resistance).
The adaptive process in these (and other) scenarios is often subject to strong stochasticity. Well-adapted genotypes are often initially rare, either because they were selected against in the previous environment and segregate only at low frequency in the population or because they appear de novo through mutation, recombination, horizontal gene transfer etc.. Despite of having a selective advantage, they may therefore suffer stochastic loss instead of establish. Much of our work turns around this stochastic establishment process.
We mostly develop population genetics models but we also work on epidemiological models.
A focus of our research is on models of evolutionary rescue, where evolution and ecology are inherently intertwined. Environmental change, if severe, can drive a population extinct unless the population succeeds in adapting to the new conditions fast enough ("evolutionary rescue"). We investigate how genetic and environmental factors such as recombination, selfing, population structure, or competition among individuals influence the probability of population survival.
The evolution of antimicrobial resistance during treatment is an instance of (undesired) evolutionary rescue. Using within-host and between-host models, we study how various treatment strategies such as combination therapy or drug dosage influence the emergence and spread of resistance to antibiotics and other antimicrobial drugs.
One of the main tools in our research is provided by branching process theory. Branching processes are a special class of stochastic processes with a discrete state space. Their characteristic property is that individuals reproduce independently from each other. This allows to treat many problems analytically.