Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods.

Jiang R, Singh P, Wrede F, Hellander A, Petzold L

PLoS Comput Biol 18 (1) e1009830 [2022-01-00; online 2022-01-31]

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.

Prashant Singh

SciLifeLab Fellow

PubMed 35100263

DOI 10.1371/journal.pcbi.1009830

Crossref 10.1371/journal.pcbi.1009830

pii: PCOMPBIOL-D-21-00833
pmc: PMC8830701

Publications 9.5.0