From Computational Biology Group
We recently published Pascal (Pathway scoring algorithm), a tool that allows gene and pathway-level analysis of GWAS association results without the need to access the original genotypic data. Pascal was designed to be fast, accurate and to have high power to detect relevant pathways. 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. The paper is available in Plos Computational Biology .
We recently collaborated with the Hafen group in Zurich on a project to identify natural variants impacting size in Drosophila. We found an association in the kek1 locus, a well-characterized growth regulator. Additionally 33 novel loci were validated. The paper is available in Plos Genetics .
Together with the lab of Sophie Martin at DMF, we showed that the intracellular gradient of Pom1 in fission yeast achieves robustness to fluctuation through intermolecular auto-phosphorylation. Gradient robustness, how molecular gradient can convey precise positional information despite large fluctuations in molecular dynamics, has been the subject of many conjectures in the last decades. In particular it was hypothesized in 2003 that such robustness could be achieved by super-linear decay. In this work we show that in the Pom1 gradient, super-linear decay is obtained by a very simple and elegant mechanism namely intermolecular auto-phosphorylation. This provides a first telling example of gradient robustness through super-linear decay through auto-catalysis, which could be a widespread phenomenon. The paper is available in in Molecular Systems Biology.
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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.