Welcome to the Computational Biology Group!
The Computational Biology Group (CBG) is part of the Department of Computational Biology (formerly Department of Medical Genetics) at the University of Lausanne. We have interest in various fields related to Computational Biology, which are detailed in the Science section of this wiki. Briefly, there are 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 and metabolomics data, as well as organismal phenotypes (ranging from patient data to growth assays). 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. For example, regulatory networks evolved to function reliably under ever-changing environmental conditions. This notion of robustness can guide computational analysis and provide constraints on models that complement those from direct measurements of the system's output.
In general, our group seeks an interdisciplinary approach, bridging the traditional gaps between physics, mathematics and biology. Our lab collaborates with experimental groups within and outside our department. In particular, due to our proximity to the University Hospital (CHUV) we have close contacts to medical research groups and assist the analysis of clinical data.
General info on this wiki
This wiki is the main instrument to centralize and archive information on and generated by the CBG. Ask Micha if you have any questions or need an account.
G-protein coupled receptors (GPCR) constitute one of the most widespread class of receptors used by cell to get information on their external environment. However it was still debated whether the GPCR signaling system can measure more that the presence or absence of a compound binding to the receptor. Together with the team of Vladimir Katanaev of the Department of Pharmacology and Toxicology at UNIL, we quantified, using tools from information theory, the amount of information that the GPCR system can provide to a cell and showed that it can distinguish between multiple concentration levels. The paper has just been published in Nature Communications.
We invented ''Metabomatching'' as a method to identify candidate compounds from genetic associations to NMR features. In our recently published paper in Plos Computational Biology we extensively tested our approach on a large collection of real GWAS association results and saw better discovery of confirmed pathways than with other popular methods.
We just published a new method for discovering new pioneer transcription factors, i.e., transcription factors that play an important role in the establishment and maintenance of open chromatin. The method basically associates transcription factor expression with chromatin accessibility genome-wide in multiple cell lines. Applied to the ENCODE data, it rediscovers known pioneer transcription factors along with new ones. It shows yet again that novel biological insights can be obtained by sophisticated analysis of large-scale public data sets. The paper is published in Plos Computational Biology .
Using gene expression data and other genomic information we constructed 394 cell type and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity between transcription factors, enhancers, promoters and genes. Each of these networks describes hundreds of thousands of regulatory interactions among thousands of genes, giving the first global view of the “control system” of cells and tissues. We found that genetic variants associated with human diseases disrupt components of these networks in disease-relevant tissues, giving new insights on disease mechanisms, which may lead to personalised treatments that are more effective and have fewer side effects. The paper is published in Nature Methods.