From Computational Biology Group
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.
Plants such as Arabidopsis Thaliana orient towards the light, thus optimizing the source of energy. This so-called phototropic response is mediated by the formation of a gradient of the plant growth hormone auxin. Using computational models validated by biological experiments, we showed in collaboration with the group of Christian Fankhauser from the CIG at UNIL, that the proton pump plays a crucial role in the establishment of this gradient and that this pump is regulated by the plants photoreceptors. The paper has just been published and is available in Molecular Systems Biology
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.
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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 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.