Regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical reaction networks

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A sparse parameter matrix estimation method is proposed for identifying a stochastic monomolecular biochemical reaction network system. Identification of a reaction network can be achieved by estimating a sparse parameter matrix containing the reaction network structure and kinetics information. Stochastic dynamics of a biochemical reaction network system is usually modeled by a chemical master equation (CME) describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular reaction systems for which an exact analytical solution of the corresponding chemical master equation can be derived. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized. A simulation result is provided to verify performance improvement of regularized MLE over least-square estimation (LSE), which is based on a deterministic mass-average model, in the case of a small population size. (C) 2016 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2016-07
Language
English
Article Type
Article
Citation

COMPUTERS & CHEMICAL ENGINEERING, v.90, pp.111 - 120

ISSN
0098-1354
DOI
10.1016/j.compchemeng.2016.03.018
URI
http://hdl.handle.net/10203/209714
Appears in Collection
CBE-Journal Papers(저널논문)
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