Team 32 : Statistical Genomics and Biological Physics

Advances in sequencing and other high-throughput experimental technologies have fuelled the genomics revolution over the past 10 to 15 years, providing an unprecedented amount of large-scale biological data. Extracting information from these data, and gaining a thorough understanding of the corresponding biological system, requires the resolution of difficult inference problems that are insoluble with conventional computer tools. Our group, newly created in September 2011, draws on the statistical physics of disordered systems to develop new algorithmic tools to solve large-scale optimization and inference tasks, in order to bring the full benefits of these computational methods to biological research. The specific issues of interest to us are the following:

• Statistical physics-based approaches for large-scale network inference
• Protein co-evolution and structural inference of proteins, their complexes and specificity of interaction
• Inference of signal transduction networks from multiple drug disruption experiments
• Robust aggregation of noisy data

    Website: http://www.lcqb.upmc.fr/StatGenomicsBiolPhys#Research Affiliations: Lab of Computational and Quantitative Biology, UMR-7238, UPMC Contact : martin.weigt@upmc.fr

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