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dc.contributor.authorBowness, Ruth
dc.contributor.authorChaplain, Mark A.J.
dc.contributor.authorPowathil, Gibin G.
dc.contributor.authorGillespie, Stephen H.
dc.date.accessioned2018-03-21T11:30:05Z
dc.date.available2018-03-21T11:30:05Z
dc.date.issued2018-06-07
dc.identifier.citationBowness , R , Chaplain , M A J , Powathil , G G & Gillespie , S H 2018 , ' Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment : insights from a hybrid multiscale cellular automaton model ' , Journal of Theoretical Biology , vol. 446 , pp. 87-100 . https://doi.org/10.1016/j.jtbi.2018.03.006en
dc.identifier.issn0022-5193
dc.identifier.otherPURE: 252493272
dc.identifier.otherPURE UUID: 9761399e-957c-4ab7-a262-eb5e946be073
dc.identifier.otherRIS: urn:7131F6CE6432A3DCCB70FB67784A50E5
dc.identifier.otherScopus: 85043488654
dc.identifier.otherORCID: /0000-0001-6537-7712/work/42504525
dc.identifier.otherORCID: /0000-0001-5727-2160/work/55378909
dc.identifier.otherORCID: /0000-0002-4090-5168/work/59698739
dc.identifier.otherWOS: 000430995800008
dc.identifier.urihttp://hdl.handle.net/10023/12983
dc.descriptionThis work was supported by the Medical Research Council [grant number MR/P014704/1] and the PreDiCT-TB consortium (IMI Joint undertaking grant agreement number 115337, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.en
dc.description.abstractIf improvements are to be made in tuberculosis (TB) treatment, an increased understanding of disease in the lung is needed. Studies have shown that bacteria in a less metabolically active state, associated with the presence of lipid bodies, are less susceptible to antibiotics, and recent results have highlighted the disparity in concentration of different compounds into lesions. Treatment success therefore depends critically on the responses of the individual bacteria that constitute the infection. We propose a hybrid, individual-based approach that analyses spatio-temporal dynamics at the cellular level, linking the behaviour of individual bacteria and host cells with the macroscopic behaviour of the microenvironment. The individual elements (bacteria, macrophages and T cells) are modelled using cellular automaton (CA) rules, and the evolution of oxygen, drugs and chemokine dynamics are incorporated in order to study the effects of the microenvironment in the pathological lesion. We allow bacteria to switch states depending on oxygen concentration, which affects how they respond to treatment. This is the first multiscale model of its type to consider both oxygen-driven phenotypic switching of the Mycobacterium tuberculosis and antibiotic treatment. Using this model, we investigate the role of bacterial cell state and of initial bacterial location on treatment outcome. We demonstrate that when bacteria are located further away from blood vessels, less favourable outcomes are more likely, i.e. longer time before infection is contained/cleared, treatment failure or later relapse. We also show that in cases where bacteria remain at the end of simulations, the organisms tend to be slower-growing and are often located within granulomas, surrounded by caseous material.
dc.language.isoeng
dc.relation.ispartofJournal of Theoretical Biologyen
dc.rights© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/ )en
dc.subjectTuberculosisen
dc.subjectCellular automatonen
dc.subjectHybrid multiscale modelen
dc.subjectAntibioticsen
dc.subjectBacteriaen
dc.subjectRA0421 Public health. Hygiene. Preventive Medicineen
dc.subjectDASen
dc.subjectBDCen
dc.subject.lccRA0421en
dc.titleModelling the effects of bacterial cell state and spatial location on tuberculosis treatment : insights from a hybrid multiscale cellular automaton modelen
dc.typeJournal articleen
dc.contributor.sponsorMedical Research Councilen
dc.contributor.sponsorEuropean Commissionen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Infection and Global Health Divisionen
dc.contributor.institutionUniversity of St Andrews. Gillespie Groupen
dc.contributor.institutionUniversity of St Andrews. Applied Mathematicsen
dc.contributor.institutionUniversity of St Andrews. Global Health Implementation Groupen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.contributor.institutionUniversity of St Andrews. Infection Groupen
dc.identifier.doihttps://doi.org/10.1016/j.jtbi.2018.03.006
dc.description.statusPeer revieweden
dc.identifier.grantnumberMR/P014704/1en
dc.identifier.grantnumberen


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