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Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion

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Title: Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion
Authors: Fröhlich, F
Thomas, P
Kazeroonian, A
Theis, FJ
Grima, R
Hasenauer, J
Item Type: Journal Article
Abstract: Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity.
Issue Date: 22-Jul-2016
Date of Acceptance: 23-Jun-2016
URI: http://hdl.handle.net/10044/1/38720
DOI: http://dx.doi.org/10.1371/journal.pcbi.1005030
ISSN: 1553-734X
Publisher: Public Library of Science
Journal / Book Title: PLOS Computational Biology
Volume: 12
Issue: 7
Copyright Statement: © 2016 Fröhlich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Sponsor/Funder: Royal Commission for the Exhibition of 1851
Keywords: Bioinformatics
06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Publication Status: Published
Article Number: e1005030
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics