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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Linking stochastic dynamic biological models to da
ta: Bayesian inference for parameters and structur
e - Darren Wilkinson (Newcastle University)
DTSTART;TZID=Europe/London:20160119T114500
DTEND;TZID=Europe/London:20160119T123000
UID:TALK64649AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/64649
DESCRIPTION:Within the field of systems biology there is incre
asing interest in developing computational models
which simulate the dynamics of intra-cellular bi
ochemical reaction networks and incorporate the st
ochasticity inherent in such processes. These mod
els can often be represented as nonlinear multivar
iate Markov processes. Analysing such models\, co
mparing competing models and fitting model parame
ters to experimental data are all challenging prob
lems. This talk will provide an overview of a Bay
esian approach to the problem. Since the models a
re typically intractable\, use is often made of al
gorithms exploiting forward simulation from the m
odel in order to render the analysis "likelihood f
ree". There have been a number of recent developm
ents in the literature relevant to this problem\,
involving a mixture of sequential and Markov chai
n Monte Carlo methods. Particular emphasis will b
e placed on the problem of Bayesian parameter inf
erence for the rate constants of stochastic b ioch
emical network models\, using noisy\, partial hig
h-resolution time course data\, such as that obtai
ned from single-cell fluorescence microscopy stud
ies.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
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