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MCMC Techniques for Parameter Estimation of ODE Based Models in Systems Biology

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cris.virtual.departmentFraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
cris.virtual.orcid0000-0002-5328-1243
cris.virtualsource.department5e5daa61-1084-42d6-881f-539cdce7be34
cris.virtualsource.orcid5e5daa61-1084-42d6-881f-539cdce7be34
crisou.acronymSCAI
dc.contributor.authorValderrama-Bahamóndez, Gloria I.
dc.contributor.authorFröhlich, Holger
dc.date.accessioned2022-03-06T05:34:37Z
dc.date.available16.4.2020
dc.date.issued2019
dc.description.abstractOrdinary differential equation systems (ODEs) are frequently used for dynamical system modeling in many science fields such as economics, physics, engineering, and systems biology. A special challenge in systems biology is that ODE systems typically contain kinetic rate parameters, which are unknown and have to be estimated from data. However, non-linearity of ODE systems together with noise in the data raise severe identifiability issues. Hence, Markov Chain Monte Carlo (MCMC) approaches have been frequently used to estimate posterior distributions of rate parameters. However, designing a good MCMC sampler for high dimensional and multi-modal parameter distributions remains a challenging task. Here we performed a systematic comparison of different MCMC techniques for this purpose using five public domain models. The comparison included Metropolis-Hastings, parallel tempering MCMC, adaptive MCMC, and parallel adaptive MCMC. In conclusion, we found specifically parallel adaptive MCMC to produce superior parameter estimates while benefitting from inclusion of our suggested informative Bayesian priors for rate parameters and noise variance.
dc.description.issue5
dc.description.startpageArt. 55, 10 S.
dc.identifier.doi10.3389/fams.2019.00055
dc.identifier.doi10.24406/publica-r-261862
dc.identifier.urihttps://publica.fraunhofer.de/handle/publica/261862
dc.language.isoen
dc.relation.grantnoBEA-2010-017
dc.relation.ispartofFrontiers in applied mathematics and statistics
dc.relation.projectSENACYT
dc.rights.licenseCC BY 4.0
dc.subjectBayesian inference
dc.subjectparameter estimation
dc.subjectODE models
dc.subjectMetropolis-Hastings
dc.subjectadaptive MCMC
dc.subjectparallel tempering MCMC
dc.subjectlikelihood computation
dc.subject.ddc003
dc.subject.ddc005
dc.subject.ddc006
dc.subject.ddc518
dc.titleMCMC Techniques for Parameter Estimation of ODE Based Models in Systems Biology
dc.typejournal article
dspace.entity.typePublication
oairecerif.acronymSENACYT
oairecerif.author.affiliationBonn-Aachen International Center for IT, University of Bonn, Bonn, Germany and Research Department, Universidad Tecnológica de Panamá, Panama City, Panama
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.funderDeutscher Akademischer Austauschdienst DAAD
oairecerif.internalidBEA-2010-017
publica.author.alternativeaffiliationBonn-Aachen International Center for IT, University of Bonn, Bonn, Germany and Research Department, Universidad Tecnológica de Panamá, Panama City, Panama
publica.author.alternativeaffiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
publica.contributor.correspondingtrue
publica.contributor.corresponding#PLACEHOLDER_PARENT_METADATA_VALUE#
publica.date13.04.2020
publica.date.scupdated2025-03-27
publica.fhg.departmentBioinformatik
publica.fhg.instituteFraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
publica.fhg.location#PLACEHOLDER_PARENT_METADATA_VALUE#
publica.identifier.urnurn:nbn:de:0011-n-5854036
publica.mig.projectSENACYT |n BEA-2010-017 |f Deutscher Akademischer Austauschdienst DAAD
publica.mig.recordnumber284922
publica.mig.urihttp://docserver.fhg.de/2020/N-585403.pdf
publica.mig.urnstypeVolltext
publica.oa.url10.3389/fams.2019.00055
publica.rights.oaOpen Access
publica.rights.oaStatusNone
publica.rights.oaUnpaywallNone
publica.rights.timestamp2025-07-01 23:06:22.088944

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