Robust Inference for Systems and Synthetic Biology

Prof. Michael Stumpf, Theoretical Systems Biology, Imperial College London

Location: LMCB, Time: 16:30

For the vast majority of biological systems we have, at best, only a rough idea as to the underlying molecular machinery. I will discuss recent developments in Bayesian Inference that allow us to reverse engineer the structure and dynamics of biological systems from observed data. Approximate Bayesian computation and graphical modelling can be used to elucidate signal transduction and gene regulation networks. Furthermore we can quantify the reliability of these inferences, and I will then show how this knowledge can be used in order to interfere with these systems rationally. In the applications I will pay particular attention to inferring dynamics from single cell data, and discuss how best to tinker with such systems.

Michael Stumpf is Professor for Theoretical Systems Biology at Imperial College London. Originally trained in mathematics and theoretical physics he has been working in evolutionary and systems biology since 1999. His research group uses statistical modelling, comparative and evolutionary information, and text mining in order to reverse-engineer the structure, dynamics and evolutionary history of biological systems in prokaryotes and eukaryotes.


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