Linking stochastic dynamic biological models to data: Bayesian inference for parameters and structure

Add to your list(s) Download to your calendar using vCal

• Darren Wilkinson (Newcastle University)
• Tuesday 19 January 2016, 11:45-12:30
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

This talk has been canceled/deleted

Within the field of systems biology there is increasing interest in developing computational models which simulate the dynamics of intra-cellular biochemical reaction networks and incorporate the stochasticity inherent in such processes. These models can often be represented as nonlinear multivariate Markov processes. Analysing such models, comparing competing models and fitting model parameters to experimental data are all challenging problems. This talk will provide an overview of a Bayesian approach to the problem. Since the models are typically intractable, use is often made of algorithms exploiting forward simulation from the model in order to render the analysis “likelihood free”. There have been a number of recent developments in the literature relevant to this problem, involving a mixture of sequential and Markov chain Monte Carlo methods. Particular emphasis will be placed on the problem of Bayesian parameter inference for the rate constants of stochastic b iochemical network models, using noisy, partial high-resolution time course data, such as that obtained from single-cell fluorescence microscopy studies.

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

This talk is not included in any other list

Note that ex-directory lists are not shown.