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dc.contributor.authorBurton, Joshua
dc.contributor.authorManning, Cerys S.
dc.contributor.authorRattray, Magnus
dc.contributor.authorPapalopulu, Nancy
dc.contributor.authorKursawe, Jochen
dc.identifier.citationBurton , J , Manning , C S , Rattray , M , Papalopulu , N & Kursawe , J 2021 , ' Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data ' , Journal of the Royal Society Interface , vol. 18 , no. 182 , 20210393 .
dc.identifier.otherPURE: 276100058
dc.identifier.otherPURE UUID: a4d40a72-0cb9-4aaf-ac97-0e2263ce0298
dc.identifier.otherJisc: a8ec52d5ebc0452a8ec9e4087af367fb
dc.identifier.otherpublisher-id: rsif20210393
dc.identifier.otherORCID: /0000-0002-0314-9623/work/100901655
dc.identifier.otherScopus: 85117335061
dc.identifier.otherWOS: 000700841900002
dc.descriptionThis work was supported by a Wellcome Trust Four-Year PhD Studentship in Basic Science to J.B. (219992/Z/19/Z) and a Wellcome Trust Senior Research Fellowship to N.P. (090868/Z/09/Z). C.M. was supported by a Sir Henry Wellcome Fellowship (103986/Z/14/Z) and University of Manchester Presidential Fellowship. M.R.’s work was supported by a Wellcome Trust Investigator Award (204832/B/16/Z).en
dc.description.abstractGene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time-series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.
dc.relation.ispartofJournal of the Royal Society Interfaceen
dc.rightsCopyright © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.en
dc.subjectPrameter inferenceen
dc.subjectBayesian methodsen
dc.subjectGene expression oscillationsen
dc.subjectKalman filtersen
dc.subjectStem cell differentiationen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleInferring kinetic parameters of oscillatory gene regulation from single cell time-series dataen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. Applied Mathematicsen
dc.description.statusPeer revieweden

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