From Computational Biology Group
In collaboration with the group of Christian Fankhauser at CIG, UNIL, we developed the HypoPhen software for the high throughput quantification of seedling elongation and bending from time-lapsed images. Using this tool, hundreds of Arabidopsis seedlings were measured to show that phytochrome A in the nucleus is important for phototropism. The results have been published in Plant Cell on February 28 2012.
A genome-wide association study by the HYPERGENES Consortium unravelled a novel hypertension susceptibility locus in the promoter region of the eNOS gene and essential hypertension. The article appeared online in Hypertension on 19 December 2011.
In a recent work, we developed a general formalism allowing to model diffusive gradient formation from an arbitrary source. This formalism applies to various diffusion problems and we illustrate our theory with the explicit example of the Bicoid gradient establishment in Drosophila embryos. The article appeared online in Journal of Theoretical Biology on 10 November 2011.
A history of all news can be found here.
Welcome to the Computational Biology Group!
The Computational Biology Group (CBG) is part of the 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 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. 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. Drop an email to the admin if you have any questions or need an account.