[simbad]

Thanks to modern high-throughput techniques, biological systems across multiple scales –from single molecules up to entire populations– can now be probed quantitatively at high spatial and temporal resolutions. These data potentially encode a plethora of information about the physical and functional constraints that govern the evolution and limit the performance of these systems, as well as about levels of organization, predictability niches or design principles that would be hard to identify from low-throughput data. Extracting this information is especially crucial for applications,  ranging from the design of proteins with a desired functionality to the tracing of contacts during an epidemics. Inverse statistical mechanics attempts to do it by inferring generative models from data using methods from the statistical mechanics of disordered systems. Specific characteristics of biological data however, like strong undersampling and heterogeneity, limit the effectiveness of these tools. SIMBAD aims at developing a class of statistical inference techniques capable of overcoming these issues. Learn more about our research