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- Chair in Computational Biology Systems Biology
- UCL Genetics Institute
- Div of Biosciences
- Faculty of Life Sciences
I studied at the University of Lausanne in Switzerland and obtained a Masters and PhD from the same institution in 1996, and 2000, respectively. After a brief stay at the University of Bern, I moved to Edinburgh, first as a postdoctoral research associate and then as a postdoctoral fellow.
At the end of 2002, I was offered a lectureship in the Department of Genetics in Cambridge. I built up a successful research group and stayed there for five years. In 2007, I decided to leave Cambridge to join the newly formed MRC Centre for Outbreak Analysis and Modelling within the Department of Infectious disease epidemiology at Imperial College London. I again spent five years there as a Reader and left in 2012 to become professor of Computational Systems Biology at UCL.
In order to extract meaningful biological information from genomic data, we need dedicated methodological tools. This is where we fit in and our mission statement is to harness genomic information by developing, refining and applying computational tools to genomic datasets to address important and interesting scientific questions.
Our core interest is to use genomic data to reconstruct past population history of a variety of organisms. One research focus in the lab is on the reconstruction of human colonisation of the world. One key motivation behind this work is to generate “null models” for the current distribution of genetic diversity, which allow controlling for the effect of past demography when making inference on genes that may have been affected by natural selection.
We also work on the reconstruction of infectious disease outbreaks and epidemics on a series of human (MRSA, the plague, malaria, influenza) and wildlife pathogens (Batrachochytrium dendrobatidis, ranavirus). The focus of this work is increasingly on why some lineages have been more successful than others. In this context, one aspect we are increasingly getting interested in is the prediction of drug resistance and their associated fitness costs from genome sequence data.
Our work spans a large spectrum ranging from the fundamental (e.g. reconstructing historical plague pandemics) to the applied (e.g. tracking MRSA infections in hospital wards). Indeed, we do not feel there must be a divide between fundamental and applied science, and while our research is primarily driven by scientific curiosity, we aim at contributing with our work to the genomic revolution in medicine, public health and conservation biology.