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How mechanistic in silico modelling can improve our understanding of TB disease and treatment

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dc.contributor.authorPitcher, Michael John
dc.contributor.authorDobson, Simon Andrew
dc.contributor.authorKelsey, Tom
dc.contributor.authorChaplain, Mark Andrew Joseph
dc.contributor.authorSloan, Derek James
dc.contributor.authorGillespie, Stephen Henry
dc.contributor.authorBowness, Ruth
dc.date.accessioned2020-11-11T15:30:07Z
dc.date.available2020-11-11T15:30:07Z
dc.date.issued2020-11-01
dc.identifier.citationPitcher , M J , Dobson , S A , Kelsey , T , Chaplain , M A J , Sloan , D J , Gillespie , S H & Bowness , R 2020 , ' How mechanistic in silico modelling can improve our understanding of TB disease and treatment ' , International Journal of Tuberculosis and Lung Disease , vol. 24 , no. 11 , pp. 1145-1150 . https://doi.org/10.5588/ijtld.20.0107en
dc.identifier.issn1027-3719
dc.identifier.otherPURE: 269148390
dc.identifier.otherPURE UUID: c847b1ec-88e1-4f6a-96c3-cfabc7b7c89c
dc.identifier.otherORCID: /0000-0002-8091-1458/work/83481726
dc.identifier.otherORCID: /0000-0002-4090-5168/work/83481808
dc.identifier.otherORCID: /0000-0001-6537-7712/work/83481823
dc.identifier.otherORCID: /0000-0001-5727-2160/work/83481901
dc.identifier.otherORCID: /0000-0001-9633-2103/work/83482096
dc.identifier.otherScopus: 85096029555
dc.identifier.otherWOS: 000601098500003
dc.identifier.otherORCID: /0000-0002-7888-5449/work/83481952
dc.identifier.urihttps://hdl.handle.net/10023/20951
dc.descriptionFunding: Medical Research Council [grant number MR/P014704/1], The Academy of Medical Sciences (AMS), the Wellcome Trust, the Government Department of Business, Energy and Industrial Strategy (BEIS), the British Heart Foundation and the Global Challenges Research Fund (GCRF) [grant number SBF003\1052] 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.abstractTB is one of the top 10 causes of death worldwide and the leading cause of death from a single infectious agent. Decreasing the length of time for TB treatment is an important step towards the goal of reducing mortality. Mechanistic in silico modelling can provide us with the tools to explore gaps in our knowledge, with the opportunity to model the complicated within-host dynamics of the infection, and simulate new treatment strategies. Significant insight has been gained using this form of modelling when applied to other diseases – much can be learned in infection research from these advances.
dc.format.extent6
dc.language.isoeng
dc.relation.ispartofInternational Journal of Tuberculosis and Lung Diseaseen
dc.rightsCopyright © 2020 Pitcher et al. 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.subjectTB modellingen
dc.subjectDisease modelsen
dc.subjectStatistical modellingen
dc.subjectWithin-host mechanistic modelen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRA0421 Public health. Hygiene. Preventive Medicineen
dc.subjectRC Internal medicineen
dc.subjectNDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subject.lccQA75en
dc.subject.lccRA0421en
dc.subject.lccRCen
dc.titleHow mechanistic in silico modelling can improve our understanding of TB disease and treatmenten
dc.typeJournal articleen
dc.contributor.sponsorMedical Research Councilen
dc.contributor.sponsorAcademy of Medical Sciencesen
dc.contributor.sponsorEuropean Commissionen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Centre for Interdisciplinary Research in Computational Algebraen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Applied Mathematicsen
dc.contributor.institutionUniversity of St Andrews. Infection and Global Health Divisionen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Centre for Biophotonicsen
dc.contributor.institutionUniversity of St Andrews. Global Health Implementation Groupen
dc.contributor.institutionUniversity of St Andrews. Gillespie Groupen
dc.contributor.institutionUniversity of St Andrews. Infection Groupen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.identifier.doihttps://doi.org/10.5588/ijtld.20.0107
dc.description.statusPeer revieweden
dc.identifier.grantnumberMR/P014704/1en
dc.identifier.grantnumberSBF003\1052en
dc.identifier.grantnumberen


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