Open AccessIdentification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods
Author(s) -
Richard Jiang,
Prashant Singh,
Fredrik Wrede,
Andreas Hellander,
Linda R. Petzold
Publication year - 2022
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1009830
Subject(s) - computer science , inference , dynamical systems theory , bayesian inference , systems biology , identification (biology) , bayesian probability , dynamic bayesian network , action (physics) , living systems , artificial intelligence , machine learning , biological system , computational biology , biology , ecology , physics , quantum mechanics
Identifying the reactions that govern a dynamical biological system is a crucial but challenging task in systems biology. In this work, we present a data-driven method to infer the underlying biochemical reaction system governing a set of observed species concentrations over time. We formulate the problem as a regression over a large, but limited, mass-action constrained reaction space and utilize sparse Bayesian inference via the regularized horseshoe prior to produce robust, interpretable biochemical reaction networks, along with uncertainty estimates of parameters. The resulting systems of chemical reactions and posteriors inform the biologist of potentially several reaction systems that can be further investigated. We demonstrate the method on two examples of recovering the dynamics of an unknown reaction system, to illustrate the benefits of improved accuracy and information obtained.