Evolutionary theory is the conceptual foundation of the life sciences. The famous geneticist Theodosius
Dobzhansky expressed this very well when he said, "Nothing in biology makes sense, except in the light of
evolution". In the post-genomic era this insight is more relevant than ever, and only by taking the theory of
evolution into account is it possible to get a handle on organizing and analyzing the massive amount of biological data
now available. In the Molecular Evolution Group we are interested in applying phylogenetic methods to analyze specific
biological systems, but also in using the flood of sequence data to learn about the evolutionary process itself.
A focus of much of the research done in the Molecular Evolution Group is the evolution of
pathogenic organisms such as HIV, influenza, and the malaria parasite Plasmodium falciparum.
In particular, we are interested in how knowledge of the evolutionary processes that occur during
infection and transmission of a pathogen can be used as the basis for deriving strategies for
fighting the disease. As an example, patterns of sequence variation across a population of influenza
viruses close to the time when the virus has jumped from, say, an avian to a human host, will contain
information about the selective pressures acting at that point. This knowledge can be directly useful
for monitoring when new species jumps are imminent but may also form the basis for designing
interventions aimed at hindering such jumps. Our work on pathogen evolution is done in close
collaboration with experimental groups at a number of universities and hospitals.
Other projects in the Molecular Evolution Group include investigations into the evolution of resistance to
antibiotics, evolution and origin of introns, de novo evolution of genes, and evolution of evolvability (the ability of
biological systems to evolve). Generally, we are interested in all aspects of evolution, and while we are very
interested in developing and applying state-of-the-art computational tools in our work (especially in the framework
of probabilistic model selection and multimodel inference), the focus is always on analyzing problems that are
interesting from a biological point of view.