Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology
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.
Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.
If you think you should have access to this title, please contact your librarian.