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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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009830","type":"journal-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T18:37:41Z","timestamp":1643654261000},"page":"e1009830","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":17,"title":["Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6994-7348","authenticated-orcid":true,"given":"Richard","family":"Jiang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3123-3478","authenticated-orcid":true,"given":"Prashant","family":"Singh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9417-6618","authenticated-orcid":true,"given":"Fredrik","family":"Wrede","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7273-7923","authenticated-orcid":true,"given":"Andreas","family":"Hellander","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6251-6078","authenticated-orcid":true,"given":"Linda","family":"Petzold","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"issue":"4","key":"pcbi.1009830.ref001","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1158\/0008-5472.CAN-03-2089","article-title":"A pivotal role of cyclic AMP-responsive element binding protein in tumor progression","volume":"64","author":"R Abramovitch","year":"2004","journal-title":"Cancer research"},{"key":"pcbi.1009830.ref002","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1385\/1-59259-773-4:067","volume-title":"Flow Cytometry Protocols","author":"OD Perez","year":"2004"},{"issue":"7189","key":"pcbi.1009830.ref003","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1038\/nature06884","article-title":"The complete genome of an individual by massively parallel DNA sequencing","volume":"452","author":"DA Wheeler","year":"2008","journal-title":"nature"},{"issue":"1","key":"pcbi.1009830.ref004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-018-2217-z","article-title":"Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data","volume":"19","author":"S Chen","year":"2018","journal-title":"BMC bioinformatics"},{"key":"pcbi.1009830.ref005","first-page":"1","volume-title":"BMC bioinformatics","author":"AA Margolin","year":"2006"},{"issue":"1","key":"pcbi.1009830.ref006","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1214\/16-AOAS990","article-title":"Gene network reconstruction using global-local shrinkage priors","volume":"11","author":"GG Leday","year":"2017","journal-title":"The annals of applied statistics"},{"issue":"9","key":"pcbi.1009830.ref007","first-page":"1","article-title":"Inferring regulatory networks from expression data using tree-based methods","volume":"5","author":"VA Huynh-Thu","year":"2010","journal-title":"PloS one"},{"issue":"1","key":"pcbi.1009830.ref008","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/TMBMC.2016.2633265","article-title":"Inferring biological networks by sparse identification of nonlinear dynamics","volume":"2","author":"NM Mangan","year":"2016","journal-title":"IEEE Transactions on Molecular, Biological and Multi-Scale Communications"},{"key":"pcbi.1009830.ref009","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.compchemeng.2016.04.019","article-title":"Inference of chemical reaction networks using mixed integer linear programming","volume":"90","author":"MJ Willis","year":"2016","journal-title":"Computers & Chemical Engineering"},{"issue":"18","key":"pcbi.1009830.ref010","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1093\/bioinformatics\/btq421","article-title":"On reverse engineering of gene interaction networks using time course data with repeated measurements","volume":"26","author":"ER Morrissey","year":"2010","journal-title":"Bioinformatics"},{"key":"pcbi.1009830.ref011","doi-asserted-by":"crossref","unstructured":"Pan W, Yuan Y, Gon\u00e7alves J, Stan GB. 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