ArticlePLoS computational biology2022
Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Bayesian inference of gene regulatory networks at stochastic steady state.Journal of the Royal Society, Interface · 2026Article
- From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode.PLoS computational biology · 2026Article
- Bayesian Inference of Gene Regulatory Networks at Stochastic Steady State.bioRxiv : the preprint server for biology · 2026Article
- Data-driven discovery of digital twins in biomedical research.Briefings in bioinformatics · 2026Review
- Inferring structure and parameters of stochastic reaction networks with logistic regression.PloS one · 2026Article
- Article
- Increasing certainty in systems biology models using Bayesian multimodel inference.Nature communications · 2025Article
- Derivative-Free Domain-Informed Data-Driven Discovery of Sparse Kinetic Models.Industrial & engineering chemistry research · 2025Article
- Compartmental Models Driven by Renewal Processes: Survival Analysis and Applications to SVIS Epidemic Models.Scientific reports · 2025Article
- Two wrongs do not make a right: the assumption that an inhibitor acts as an inverse activator.Journal of mathematical biology · 2024Article
- Synthetic living materials in cancer biology.Nature reviews bioengineering · 2023Article
- Distilling identifiable and interpretable dynamic models from biological data.PLoS computational biology · 2023Article
- SBbadger: biochemical reaction networks with definable degree distributions.Bioinformatics (Oxford, England) · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
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.
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