Moment-Based Parameter Estimation for Stochastic Reaction Networks in Equilibrium

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Michael BackenkohlerVerena Wolf

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.

Related Concepts

Stochastic Processes
Systems Biology
Gene Modules
Case-Control Studies
Flow Cytometry
Evaluation
Statistical Technique

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