From Computational Biology Group
In a joint work with the lab of Christian Fankhauser at CIG, UNIL, we showed that plants adapt their hormonal signal to the availability of resources when avoiding shade. If resources are scarce, the signal is weaker but the sensitivity is enhanced but when the signal is abundant, a stronger and more robust signal is produced. Our study, which thus suggests that the plant optimizes a signal cost-to-robustness trade-off, has just been published in PNAS.
In a study initiated by Sébastien Jacquemont from the Service of Medical Genetics in collaboration with the group of Evan Eichler, we investigated the CNV burden of autistic patients and close relatives. We could show that females diagnosed with autism have on average more deleterious mutations in genes involved in neuro-developmental disorders than males, hinting that women can cope with a higher mutational burden than men. Moreover most of the deleterious mutations in genes important for brain function are transmitted by the non-affected mothers, showing that they can tolerate more mutations than the fathers.This study was published in the American Journal of Human Genetics and featured in the French newspaper Le Figaro and in the Economist
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 Micha if you have any questions or need an account.