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A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorMarino, Ines P
dc.contributor.authorZaikin, Alexey
dc.contributor.authorMíguez Arenas, Joaquín
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Educación, Cultura y Deporte (España)es
dc.date.accessioned2023-10-06T09:30:10Z
dc.date.available2023-10-06T09:30:10Z
dc.date.issued2017-08-10
dc.description.abstractWe compare three state-of-the-art Bayesian inference methods for the estimation of the unknown parameters in a stochastic model of a genetic network. In particular, we introduce a stochastic version of the paradigmatic synthetic multicellular clock model proposed by Ullner et al., 2007. By introducing dynamical noise in the model and assuming that the partial observations of the system are contaminated by additive noise, we enable a principled mechanism to represent experimental uncertainties in the synthesis of the multicellular system and pave the way for the design of probabilistic methods for the estimation of any unknowns in the model. Within this setup, we tackle the Bayesian estimation of a subset of the model parameters. Specifically, we compare three Monte Carlo based numerical methods for the approximation of the posterior probability density function of the unknown parameters given a set of partial and noisy observations of the system. The schemes we assess are the particle Metropolis-Hastings (PMH) algorithm, the nonlinear population Monte Carlo (NPMC) method and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) scheme. We present an extensive numerical simulation study, which shows that while the three techniques can effectively solve the problem there are significant differences both in estimation accuracy and computational efficiency.en
dc.description.sponsorshipThis research has been partially supported by the Spanish Ministry of Economy and Competitiveness (projects TEC2015-69868-C2-1-R ADVENTURE and FIS2013-40653-P), the Spanish Ministry of Education, Culture and Sport (mobility award PRX15/00378), the Office of Naval Research (ONR) Global (Grant Award no. N62909-15-1-2011), the Cancer Research UK and the Eve Appeal Gynaecological Cancer Research Fund (grant ref. A12677) supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre. AZ thanks support from Russian Science Foundation (16-12-00077).en
dc.format.extent25es
dc.identifier.bibliographicCitationMariño, I. P., Zaikin, A., & Mı́guez, J. (2017). A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks. PLOS ONE, 12(8), e0182015.en
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0182015
dc.identifier.issn1932-6203
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue8, e0182015es
dc.identifier.publicationlastpage25es
dc.identifier.publicationtitlePLoS Oneen
dc.identifier.publicationvolume12es
dc.identifier.urihttps://hdl.handle.net/10016/38565
dc.identifier.uxxiAR/0000020434
dc.language.isoengen
dc.publisherPublic Library of Science (PLoS)en
dc.relation.projectIDGobierno de España. TEC2015-69868-C2-1-Res
dc.relation.projectIDGobierno de España. FIS2013-40653-Pes
dc.relation.projectIDGobierno de España. PRX15/00378es
dc.rights© 2017 Mariño et al.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.titleA comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networksen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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