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Moment-Based Parameter Estimation for Stochastic Reaction Networks in Equilibrium.

Authors :
Backenkohler, Michael
Bortolussi, Luca
Wolf, Verena
Source :
IEEE/ACM Transactions on Computational Biology & Bioinformatics; Jul/Aug2018, Vol. 15 Issue 4, p1180-1192, 13p
Publication Year :
2018

Abstract

Calibrating parameters is a crucial problem within quantitative modeling approaches to reaction networks. Existing methods for stochastic models rely either on statistical sampling or can only be applied to small systems. Here, we present an inference procedure for stochastic models in equilibrium that is based on a moment matching scheme with optimal weighting and that can be used with high-throughput data like the one collected by flow cytometry. Our method does not require an approximation of the underlying equilibrium probability distribution and, if reaction rate constants have to be learned, the optimal values can be computed by solving a linear system of equations. We discuss important practical issues such as the selection of the moments and evaluate the effectiveness of the proposed approach on three case studies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15455963
Volume :
15
Issue :
4
Database :
Complementary Index
Journal :
IEEE/ACM Transactions on Computational Biology & Bioinformatics
Publication Type :
Academic Journal
Accession number :
131228945
Full Text :
https://doi.org/10.1109/TCBB.2017.2775219