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dc.contributor.authorMinas, Giorgos
dc.contributor.authorRand, David A.
dc.date.accessioned2018-08-03T09:30:11Z
dc.date.available2018-08-03T09:30:11Z
dc.date.issued2017-07-24
dc.identifier.citationMinas , G & Rand , D A 2017 , ' Long-time analytic approximation of large stochastic oscillators : simulation, analysis and inference ' , PLoS Computational Biology , vol. 13 , no. 7 , e1005676 , pp. 1-23 . https://doi.org/10.1371/journal.pcbi.1005676en
dc.identifier.issn1553-734X
dc.identifier.otherPURE: 255167562
dc.identifier.otherPURE UUID: 0e5969ad-d1e5-4d96-87b9-b8d9b6c22254
dc.identifier.otherScopus: 85026660727
dc.identifier.otherPubMed: 28742083
dc.identifier.otherORCID: /0000-0001-7953-706X/work/47136660
dc.identifier.urihttp://hdl.handle.net/10023/15761
dc.descriptionThis research was funded by the BBSRC Grant BB/K003097/1 (Systems Biology Analysis of Biological Timers and Inflammation). DAR was also supported by funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 305564. BBSRC web site: www.bbsrc.ac.uk Seventh Framework Programme (FP7) website: cordis.europa.eu/fp7/home_en.html.en
dc.description.abstractIn order to analyse large complex stochastic dynamical models such as those studied in systems biology there is currently a great need for both analytical tools and also algorithms for accurate and fast simulation and estimation. We present a new stochastic approximation of biological oscillators that addresses these needs. Our method, called phase-corrected LNA (pcLNA) overcomes the main limitations of the standard Linear Noise Approximation (LNA) to remain uniformly accurate for long times, still maintaining the speed and analytically tractability of the LNA. As part of this, we develop analytical expressions for key probability distributions and associated quantities, such as the Fisher Information Matrix and Kullback-Leibler divergence and we introduce a new approach to system-global sensitivity analysis. We also present algorithms for statistical inference and for long-term simulation of oscillating systems that are shown to be as accurate but much faster than leaping algorithms and algorithms for integration of diffusion equations. Stochastic versions of published models of the circadian clock and NF-κB system are used to illustrate our results.
dc.format.extent23
dc.language.isoeng
dc.relation.ispartofPLoS Computational Biologyen
dc.rights© 2017 Minas, Rand. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.subjectEcology, Evolution, Behavior and Systematicsen
dc.subjectGeneticsen
dc.subjectMolecular Biologyen
dc.subjectComputational Theory and Mathematicsen
dc.subjectEcologyen
dc.subjectModelling and Simulationen
dc.subjectCellular and Molecular Neuroscienceen
dc.subject3rd-DASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject.lccQAen
dc.subject.lccQH301en
dc.titleLong-time analytic approximation of large stochastic oscillators : simulation, analysis and inferenceen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Statisticsen
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1005676
dc.description.statusPeer revieweden


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