From Computational Biology Group
Together with the group of Christian Fankhauser from the CIG at UNIL, CBG post-doc Tim Hohm showed that the sites of light perception for phototropism is located in the upper hypocotyl, where asymmetric elongation occurs. Thus, in contrast to monocots where a phototropism signal is sent from the leaves to the stem, in Arabidopsis it all happens "on site". The paper has just been published in Current Biology
Daniel joins the CBG after his postdoctoral fellowship in the group of Manolis Kellis at the Massachusetts Institute of Technology (MIT) and the Broad Institute. During his postdoc at MIT, he led a community-based challenge in gene regulatory network inference (the DREAM challenge) and developed novel methods to predict regulatory relationships more accurately. At the CBG, he now intends to leverage these networks to interpret genetic variants associated with complex diseases and traits in the human.
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 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. Ask user:Micha if you have any questions or need an account.