Multiscale modeling and simulations to bridge molecular and cellular scales

Cox process representation and inference for stochastic reaction-diusion processes

speaker: David Schnoerr (Imperial College London, UK)

abstract: Complex behaviour in many biological systems arises from the stochastic interactions of spatially distributed particles or agents. Examples range from gene expression to chemotaxis and epidemi- ology. Stochastic reaction-diusion processes are widely used to model such systems, yet they are notoriously dicult to simulate and calibrate to observational data. Here we use ideas from statisti- cal physics and machine learning to provide a solution to the inverse problem of learning a stochastic reaction-diusion process from data. Our solution relies on a novel, non-trivial connection between stochastic reaction-diusion processes and spatio-temporal Cox processes, a well-studied class of models from computational statistics. We develop an ecient and exible algorithm which shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Joint work with Ramon Grima and Guido Sanguinetti

timetable:
Thu 4 Oct, 9:00 - 9:45, Aula Dini
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