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dc.contributor.authorFallahi, Shirin
dc.contributor.authorSkaug, Hans J.
dc.contributor.authorAlendal, Guttorm
dc.date.accessioned2021-05-19T13:39:52Z
dc.date.available2021-05-19T13:39:52Z
dc.date.created2020-07-17T14:17:23Z
dc.date.issued2020
dc.PublishedPLOS ONE. 2020, 15:e0235393 (7), 1-24.
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/11250/2755735
dc.description.abstractReaction rates (fluxes) in a metabolic network can be analyzed using constraint-based modeling which imposes a steady state assumption on the system. In a deterministic formulation of the problem the steady state assumption has to be fulfilled exactly, and the observed fluxes are included in the model without accounting for experimental noise. One can relax the steady state constraint, and also include experimental noise in the model, through a stochastic formulation of the problem. Uniform sampling of fluxes, feasible in both the deterministic and stochastic formulation, can provide us with statistical properties of the metabolic network, such as marginal flux probability distributions. In this study we give an overview of both the deterministic and stochastic formulation of the problem, and of available Monte Carlo sampling methods for sampling the corresponding solution space. We apply the ACHR, OPTGP, CHRR and Gibbs sampling algorithms to ten metabolic networks and evaluate their convergence, consistency and efficiency. The coordinate hit-and-run with rounding (CHRR) is found to perform best among the algorithms suitable for the deterministic formulation. A desirable property of CHRR is its guaranteed distributional convergence. Among the three other algorithms, ACHR has the largest consistency with CHRR for genome scale models. For the stochastic formulation, the Gibbs sampler is the only method appropriate for sampling at genome scale. However, our analysis ranks it as less efficient than the samplers used for the deterministic formulation.en_US
dc.language.isoengen_US
dc.publisherPLOSen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA comparison of Monte Carlo sampling methods for metabolic network modelsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2020 The Authorsen_US
dc.source.articlenumbere0235393en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1371/journal.pone.0235393
dc.identifier.cristin1819726
dc.source.journalPLOS ONEen_US
dc.source.4015:e0235393
dc.source.147
dc.relation.projectNorges forskningsråd: 248840en_US
dc.identifier.citationPLOS ONE. 2020, 15(7): e0235393en_US
dc.source.volume15en_US
dc.source.issue7en_US


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