We have interest in various fields related to Computational Biology following two main directions: we develop and apply methods for the integrative analysis of large-scale biological and clinical data. This includes molecular phenotypes like gene-expression data, as well as organismal phenotypes (ranging from patient data to growth arrays). We focus particularly on relating these phenotypes to genotypes such as "Single Nucleotide Polymorphisms" (SNPs) and "Copy Number Variants" (CNVs) measured by microarrays or next-generation sequencing. Our goal is to move towards predictive models in order to improve the diagnosis, prevention and treatment of disease. A complementary direction of research pertains to relatively small genetic networks, whose components are well-known. We collaborate closely with experts of the field to identify biological systems that can be modeled quantitatively. Our goal in developing such models is not only to give an approximate description of system, but also to obtain a better understanding of its properties. Our group is affiliated to the SIB Swiss Institute of Bioinformatics.
More details on our research and teaching efforts can be found here (UniL) and here (SIB).
