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SUMMARY:Linking stochastic dynamic biological models to data: Bayesian inf
 erence for parameters and structure - Darren Wilkinson (Newcastle Universi
 ty)
DTSTART:20160119T114500Z
DTEND:20160119T123000Z
UID:TALK64649@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Within the field of systems biology there is increasing intere
 st in  developing computational models which simulate the dynamics of intr
 a-cellular  biochemical reaction networks and incorporate the stochasticit
 y inherent in such  processes. These models can often be represented as no
 nlinear multivariate  Markov processes. Analysing such models\, comparing 
 competing models and fitting  model parameters to experimental data are al
 l 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 num
 ber of recent developments in the literature relevant to  this problem\, i
 nvolving a mixture of sequential and Markov chain Monte Carlo  methods. Pa
 rticular emphasis will be placed on the problem of Bayesian parameter  inf
 erence for the rate constants of stochastic b iochemical network models\, 
  using noisy\, partial high-resolution time course data\, such as that obt
 ained  from single-cell fluorescence microscopy studies.
LOCATION:Seminar Room 1\, Newton Institute
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