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dc.contributor.authorGalagali, Nikhil
dc.contributor.authorMarzouk, Youssef M
dc.date.accessioned2020-08-03T13:07:27Z
dc.date.available2020-08-03T13:07:27Z
dc.date.issued2019-02
dc.identifier.issn1742-5689
dc.identifier.issn1742-5662
dc.identifier.urihttps://hdl.handle.net/1721.1/126467
dc.description.abstractThe development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved 'between-model' proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.en_US
dc.language.isoen
dc.publisherThe Royal Societyen_US
dc.relation.isversionof10.1098/RSIF.2018.0766en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleExploiting network topology for large-scale inference of nonlinear reaction modelsen_US
dc.typeArticleen_US
dc.identifier.citationGalagali, Nikhil and Youssef M. Marzouk. “Exploiting network topology for large-scale inference of nonlinear reaction models.” Journal of the Royal Society interface, vol. 16, no. 152, 2019, 20182766 © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.relation.journalJournal of the Royal Society interfaceen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-10-29T18:46:58Z
dspace.date.submission2019-10-29T18:47:08Z
mit.journal.volume16en_US
mit.journal.issue152en_US


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