Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
01 2022
01 2022
Historique:
received:
04
05
2021
accepted:
12
01
2022
revised:
10
02
2022
pubmed:
1
2
2022
medline:
5
3
2022
entrez:
31
1
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35100263
doi: 10.1371/journal.pcbi.1009830
pii: PCOMPBIOL-D-21-00833
pmc: PMC8830701
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1009830Subventions
Organisme : NIBIB NIH HHS
ID : R01 EB014877
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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