We aspire to unravel general principles of molecular evolution and to apply this knowledge to better understand molecular function and dysfunction, using statistical and computational methods. The key questions underlying our research are:
- How can we best extrapolate our current knowledge of molecular biology, concentrated in just a handful of model organisms, to the rest of life?
- Conversely, how can we exploit the wealth and diversity of life to better understand human biology and disease?
- Can we meaningfully summarise the evolutionary history of species into a small number of tree topologies that capture both the vertical inheritance and most important events of non-vertical inheritance?
We tackle these problems by developing statistical and computational methods and applying them to large-scale genomic data. This process combines biological aspects in the early stages (e.g. problem statement, identifying relevant empirical observations, determining dependable benchmarks and controls), statistical, algorithmic, and computational aspects in the middle (e.g. model formulation, programming, scaling up), and biological aspects again at the end in the interpretation of the results.