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Gaining Confidence in Signalling and Regulatory Networks

Mathematical models of signalling and gene regulatory systems are abstractions of much more complicated processes. Even as more and larger data sets are becoming available we are not be able to dispense entirely with mechanistic models of real-world processes; nor should we. However, trying to develop informative and realistic models of such systems typically involves suitable statistical inference methods, domain expertise and a modicum of luck. Except for cases where physical principles provide sucient guidance it will also be generally possible to come up with a large number of potential models that are compatible with a given biological system and any finite amount of data generated from experiments on that system. Here I will discuss how we can systematically evaluate potentially vast sets of mechanistic candidate models in light of experimental and prior knowledge about biological systems. This enables us to evaluate quantitatively the dependence of model inferences and predictions on the assumed model structures. Failure to consider the impact of structural uncertainty introduces biases into the analysis and potentially gives rise to misleading conclusions.

Type of Seminar:
IfA Seminar
Prof. Michael Stumpf
Theoretical Systems Biology, Imperial College London
Oct 21, 2014   11:15

ML F 34, Sonneggstrasse 3
Contact Person:

Prof. John Lygeros
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Biographical Sketch:
Michael Stumpf holds the Chair in Theoretical Systems Biology at Imperial College London, where his group is primarily concerned with inverse problems in systems and evolutionary biology. In his research he combines a broad range of statistical, mathematical and computational tools to tackle signal transduction and cell-fate decision processes in cell and molecular biology. Much of his work focuses on host-pathogen systems, immune response mechanisms and haematopoiesis and haematopoietic stem cells and their roles in health and disease.