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dc.contributor.authorHarries, Lorna W.
dc.date.accessioned2021-02-15T11:13:16Z
dc.date.available2021-02-15T11:13:16Z
dc.date.issued2019-10-31
dc.identifier.citationGalvis D et al. A dynamical systems model for the measurement of cellular senescence. J R Soc Interface. 2019 Oct 31;16(159):20190311. doi: 10.1098/rsif.2019.0311. Epub 2019 Oct 9.en_US
dc.identifier.pmid31594522
dc.identifier.doi10.1098/rsif.2019.0311
dc.identifier.urihttps://rde.dspace-express.com/handle/11287/621611
dc.description.abstractSenescent cells provide a good in vitro model to study ageing. However, cultures of 'senescent' cells consist of a mix of cell subtypes (proliferative, senescent, growth-arrested and apoptotic). Determining the proportion of senescent cells is crucial for studying ageing and developing new anti-degenerative therapies. Commonly used markers such as doubling population, senescence-associated β-galactosidase, Ki-67, γH2AX and TUNEL assays capture diverse and overlapping cellular populations and are not purely specific to senescence. A newly developed dynamical systems model follows the transition of an initial culture to senescence tracking population doubling, and the proportion of cells in proliferating, growth-arrested, apoptotic and senescent states. Our model provides a parsimonious description of transitions between these states accruing towards a predominantly senescent population. Using a genetic algorithm, these model parameters are well constrained by an in vitro human primary fibroblast dataset recording five markers at 16 time points. The computational model accurately fits to the data and translates these joint markers into the first complete description of the proportion of cells in different states over the lifetime. The high temporal resolution of the dataset demonstrates the efficacy of strategies for reconstructing the trajectory towards replicative senescence with a minimal number of experimental recordings.en_US
dc.language.isoenen_US
dc.publisherAtyponen_US
dc.relation.urlhttps://royalsocietypublishing.org/doi/10.1098/rsif.2019.0311?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmeden_US
dc.rights© 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are crediteden_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectageingen_US
dc.subjectdynamical systems modelen_US
dc.subjectfibroblasten_US
dc.subjectmodellingen_US
dc.titleA dynamical systems model for the measurement of cellular senescenceen_US
dc.typeJournal Articleen_US
dc.identifier.journalJournal of the Royal Society, Interfaceen_US
dc.identifier.pmcidPMC6833332
dc.description.noteThis article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.en_US
dc.description.fundingThis work was generously supported by the Wellcome Trust Institutional Strategic Support Award (grant no. 204909/Z/16/Z). J.R. acknowledges the financial support of the EPSRC Centre for Predictive Modelling in Healthcare (grant no. EP/N014391/1) and from an EPSRC New Investigator Award (grant no. EP/R03124X/1). E.L. acknowledges the financial support of the Dunhill Medical Trust (grant no. R386/114).en_US
dc.type.versionPublisheden_US
dc.description.admin-notepublished version, accepted versionen_US


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© 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited
Except where otherwise noted, this item's license is described as © 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited