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dc.contributor.advisorDobson, Simon
dc.contributor.authorPitcher, Michael John
dc.coverage.spatialxxxiii, 209 p.en_US
dc.date.accessioned2020-03-02T14:34:46Z
dc.date.available2020-03-02T14:34:46Z
dc.date.issued2020-06-24
dc.identifier.urihttp://hdl.handle.net/10023/19575
dc.description.abstractTuberculosis (TB) accounts for over 1 million deaths each year, despite effective treatment regimens being available. Improving the treatment of TB will require new regimens, each of which will need to be put through expensive and lengthy clinical trials, with no guarantee of success. The ability to predict which of many novel regimens to progress through the clinical trial stages would be an important tool to TB research. as current models are constrained in their ability to reflect the whole spectrum of pathophysiology, particularly as there remains uncertainty around the events that occur. This thesis explores the use of computational techniques to model a pulmonary human TB infection. We introduce the first in silico model of TB occurring over the whole lung that incorporates both the environmental heterogeneity that is exhibited within different spatial regions of the organ, and the different bacterial dissemination routes, in order to understand how bacteria move during infection and why post-primary disease is typically localised towards the apices of the lung. Our results show that including environmental heterogeneity within the lung can have profound effects on the results of an infection, by creating a region towards the apex which is preferential for bacterial proliferation. We also present a further iteration of the model, whereby the environment is made more granular to better understand the regions which are afflicted during infection, and show how sensitivity analysis can determine the factors that contribute most to disease outcomes. We show that in order to simulate TB disease within a human lung with sufficient accuracy, better understanding of the dynamics is required. The model presented in this thesis is intended to provide insight into these complicated dynamics, and thus make progress towards an end goal of a virtual clinical trial, consisting of a heterogeneous population of synthetic virtual patients.en_US
dc.description.sponsorship"“This work was supported by 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 EF-PIA companies’ in kind contribution.)” -- Acknowledgementsen
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.relationPitcher, M. J., Bowness, R., Dobson, S., & Gillespie, S. H. (2018). A spatially heterogeneous network-based metapopulation software model applied to the simulation of a pulmonary tuberculosis infection. Applied Network Science, 3(1). https://doi.org/10.1007/s41109-018-0091-2en_US
dc.relationPitcher, M. J., Bowness, R., Dobson, S., Eftimie, R., & Gillespie, S. H. (2019). Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle. BioRxiv. https://doi.org/10.1101/2019.12.12.871269en_US
dc.relation.urihttps://doi.org/10.1007/s41109-018-0091-2
dc.relation.urihttps://doi.org/10.1101/2019.12.12.871269
dc.rightsCreative Commons Attribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectTuberculosisen_US
dc.subjectSystems biologyen_US
dc.subjectNetwork modellingen_US
dc.subjectSensitivity analysisen_US
dc.subjectComputational biologyen_US
dc.subjectLungen_US
dc.subjectMetapopulationen_US
dc.titleIn silico modelling of in-host tuberculosis dynamics : towards building the virtual patienten_US
dc.typeThesisen_US
dc.contributor.sponsorPreDiCT-TB Consortiumen_US
dc.contributor.sponsorSeventh Framework Programme (European Commission)en
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US


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