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A spatially heterogeneous network-based metapopulation software model applied to the simulation of a pulmonary tuberculosis infection
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dc.contributor.author | Pitcher, Michael John | |
dc.contributor.author | Bowness, Ruth | |
dc.contributor.author | Dobson, Simon Andrew | |
dc.contributor.author | Gillespie, Stephen Henry | |
dc.date.accessioned | 2018-08-27T16:30:05Z | |
dc.date.available | 2018-08-27T16:30:05Z | |
dc.date.issued | 2018-08-23 | |
dc.identifier.citation | Pitcher , M J , Bowness , R , Dobson , S A & Gillespie , S H 2018 , ' A spatially heterogeneous network-based metapopulation software model applied to the simulation of a pulmonary tuberculosis infection ' , Applied Network Science , vol. 3 , 33 . https://doi.org/10.1007/s41109-018-0091-2 | en |
dc.identifier.issn | 2364-8228 | |
dc.identifier.other | PURE: 255191074 | |
dc.identifier.other | PURE UUID: ad8cdaa6-ad0b-4b29-8596-f69da2d0dc34 | |
dc.identifier.other | ORCID: /0000-0001-6537-7712/work/54819102 | |
dc.identifier.other | ORCID: /0000-0002-4090-5168/work/59698737 | |
dc.identifier.other | Scopus: 85065321747 | |
dc.identifier.other | ORCID: /0000-0001-9633-2103/work/70234154 | |
dc.identifier.uri | https://hdl.handle.net/10023/15886 | |
dc.description | 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 EFPIA companies’ in kind contribution. | en |
dc.description.abstract | Tuberculosis (TB) is an ancient disease that, although curable, still accounts for over 1 million deaths worldwide. Shortening treatment time is an important area of research but is hampered by the lack of models that mimic the full range of human pathology. TB shows distinct localisations during different stages of infection, the reasons for which are poorly understood. Greater understanding of how heterogeneity within the human lung influences disease progression may hold the key to improving treatment efficiency and reducing treatment times. In this work, we present a novel in silico software model which uses a networked metapopulation incorporating both spatial heterogeneity and dissemination possibilities to simulate a TB infection over the whole lung and associated lymphatics. The entire population of bacteria and immune cells is split into a network of patches: members interact within patches and are able to move between them. Patches and edges of the lung network include their own environmental attributes which influence the dynamics of interactions between the members of the subpopulations of the patches and the translocation of members along edges. In this work, we detail the initial findings of a whole-organ model that incorporates distinct spatial heterogeneity features which are not present in standard differential equation approaches to tuberculosis modelling. We show that the inclusion of heterogeneity within the lung landscape when modelling TB disease progression has significant outcomes on the bacterial load present: a greater differential of oxygen, perfusion and ventilation between the apices and the basal regions of the lungs creates micro-environments at the apex that are more preferential for bacteria, due to increased oxygen availability and reduced immune activity, leading to a greater overall bacterial load present once latency is established. These findings suggest that further whole-organ modelling incorporating more sophisticated heterogeneities within the environment and complex lung topologies will provide more insight into the environments in which TB bacteria persist and thus help develop new treatments which are factored towards these environmental conditions. | |
dc.format.extent | 21 | |
dc.language.iso | eng | |
dc.relation.ispartof | Applied Network Science | en |
dc.rights | © The Author(s). 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | en |
dc.subject | Complex networks | en |
dc.subject | Metapopulation | en |
dc.subject | Spatial heterogeneity | en |
dc.subject | Tuberculosis | en |
dc.subject | In-host modelling | en |
dc.subject | Computational biology | en |
dc.subject | QA76 Computer software | en |
dc.subject | R Medicine (General) | en |
dc.subject | NDAS | en |
dc.subject | SDG 3 - Good Health and Well-being | en |
dc.subject.lcc | QA76 | en |
dc.subject.lcc | R1 | en |
dc.title | A spatially heterogeneous network-based metapopulation software model applied to the simulation of a pulmonary tuberculosis infection | en |
dc.type | Journal article | en |
dc.contributor.sponsor | European Commission | en |
dc.description.version | Publisher PDF | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.contributor.institution | University of St Andrews. Infection and Global Health Division | en |
dc.contributor.institution | University of St Andrews. School of Medicine | en |
dc.contributor.institution | University of St Andrews. Gillespie Group | en |
dc.contributor.institution | University of St Andrews. Global Health Implementation Group | en |
dc.contributor.institution | University of St Andrews. Infection Group | en |
dc.contributor.institution | University of St Andrews. Biomedical Sciences Research Complex | en |
dc.identifier.doi | https://doi.org/10.1007/s41109-018-0091-2 | |
dc.description.status | Peer reviewed | en |
dc.identifier.grantnumber | en |
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