Document Detail

Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.
MedLine Citation:
PMID:  21551095     Owner:  NLM     Status:  MEDLINE    
We present a novel and simple method to numerically calculate Fisher information matrices for stochastic chemical kinetics models. The linear noise approximation is used to derive model equations and a likelihood function that leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher information matrix to solving a set of ordinary differential equations. This is the first method to compute Fisher information for stochastic chemical kinetics models without the need for Monte Carlo simulations. This methodology is then used to study sensitivity, robustness, and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.
Michał Komorowski; Maria J Costa; David A Rand; Michael P H Stumpf
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2011-05-06
Journal Detail:
Title:  Proceedings of the National Academy of Sciences of the United States of America     Volume:  108     ISSN:  1091-6490     ISO Abbreviation:  Proc. Natl. Acad. Sci. U.S.A.     Publication Date:  2011 May 
Date Detail:
Created Date:  2011-05-25     Completed Date:  2011-10-07     Revised Date:  2013-06-30    
Medline Journal Info:
Nlm Unique ID:  7505876     Medline TA:  Proc Natl Acad Sci U S A     Country:  United States    
Other Details:
Languages:  eng     Pagination:  8645-50     Citation Subset:  IM    
Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, United Kingdom.
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MeSH Terms
Models, Biological*
Monte Carlo Method
Stochastic Processes
Grant Support
BB/F005261/1//Biotechnology and Biological Sciences Research Council; BB/G020434/1//Biotechnology and Biological Sciences Research Council

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