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Bayesian Statistics 9$
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José M. Bernardo, M. J. Bayarri, James O. Berger, A. P. Dawid, David Heckerman, Adrian F. M. Smith, and Mike West

Print publication date: 2011

Print ISBN-13: 9780199694587

Published to Oxford Scholarship Online: January 2012

DOI: 10.1093/acprof:oso/9780199694587.001.0001

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Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology

Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology

Chapter:
(p.679) Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology
Source:
Bayesian Statistics 9
Author(s):

Darren J. Wilkinson

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199694587.003.0023

Bacteria are single‐celled organisms which often display heterogeneous behaviour, even among populations of genetically identical cells in uniform environmental conditions. Markov process models arising from the theory of stochastic chemical kinetics are often used to understand the genetic regulation of the behaviour of individual bacterial cells. However, such models often contain uncertain parameters which need to be estimated from experimental data. Parameter estimation for complex high‐dimensional Markov process models using diverse, partial, noisy and poorly calibrated time‐course experimental data is a challenging inferential problem, but a computationally intensive Bayesian approach turns out to be effective. The utility and added‐ value of the approach is demonstrated in the context of a stochastic model of a key cellular decision made by the gram‐positive bacterium Bacillus subtilis, using quantitative data from single‐cell fluorescence microscopy and flow cytometry experiments.

Keywords:   Bacillus subtilus, Genetic Regulation, GFP, Likelihood-free MCMC, Motility, Time-lapse Fluorescence Microscopy

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