Assessing mechanistic hypotheses by statistical integration of spatial imaging data and complex models

Nigel Burroughs (Systems Biology, WSBC Warwick University, UK)

Abstract: A central problem in the modelling of biological systems is the joint problem of fitting these models to data (fitting parameters) and assessing whether the assumptions and mechanisms in the model are supported by the data; the latter is essentially a model selection problem and the identification of which model (and associated hypotheses) best describe the data. This is particularly acute for spatial dynamic image data given the complexity of the models (PDEs or stochastic analogues) and the difficulty of extracting information from image data. In this talk I will examine a number of examples using Bayesian statistical techniques to answer mechanistic hypotheses in spatial data; firstly, whether the PDE models of spatially organisation in immune synapses based on exodomain size differences are consistent with experimentally observed patternation, secondly, heterogeneity of single molecule movement in cell membranes, and thirdly, an examination of the mechanistic processes that underpin kinetochore oscillations across the metaphase plate during cell division.