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dc.contributor.authorBowness, Ruth
dc.contributor.editorSchmitz, Ulf
dc.contributor.editorWolkenhauer, Olaf
dc.date.accessioned2017-01-04T00:32:46Z
dc.date.available2017-01-04T00:32:46Z
dc.date.issued2016
dc.identifier.citationBowness , R 2016 , Systems medicine and infection . in U Schmitz & O Wolkenhauer (eds) , Systems Medicine . Methods in Molecular Biology , vol. 1386 , Springer , pp. 107-118 . https://doi.org/10.1007/978-1-4939-3283-2_7en
dc.identifier.isbn9781493932825
dc.identifier.isbn9781493932832
dc.identifier.issn1064-3745
dc.identifier.otherPURE: 229012001
dc.identifier.otherPURE UUID: c39ed1f6-f88c-4ea4-93ac-9bdf1d431ff1
dc.identifier.otherScopus: 84950299591
dc.identifier.otherORCID: /0000-0002-4090-5168/work/59698736
dc.identifier.otherWOS: 000368813900008
dc.identifier.urihttp://hdl.handle.net/10023/10036
dc.description.abstractBy using a systems based approach, mathematical and computational techniques can be used to develop models that describe the important mechanisms involved in infectious diseases. An iterative approach to model development allows new discoveries to continually improve the model, and ultimately increase the accuracy of predictions. SIR models are used to describe epi demics, predicting the extent and spread of disease. Genome-wide genotyping and sequencing technologies can be used to identify the biological mechanisms behind diseases. These tools help to build strategies for disease prevention and treatment, an example being the recent outbreak of Ebola in West Africa where these techniques were deployed. HIV is a complex disease where much is still to be learnt about the virus and the best effective treatment. With basic mathematical modelling techniques, significant discoveries have been made over the last 20 years. With recent technological advances, the computation al resources now available and interdisciplinary cooperation, further breakthroughs are inevitable. In TB, modelling has traditionally been empirical in nature, with clinical data providing the fuel for this top-down approach. Recently, projects have begun to use data derived from laboratory experiments and clinical trials to create mathematical models that describe the mechanisms responsible for the disease. A systems medicine approach to infection modelling helps identify important biological questions that then direct future experiments , the results of which improve the model in an iterative cycle . This means that data from several model systems can be integrated and synthesised to explore complex biological systems .
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofSystems Medicineen
dc.relation.ispartofseriesMethods in Molecular Biologyen
dc.rights© 2016, Springer Science+Business Media New York. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.springer.com / https://dx.doi.org/10.1007/978-1-4939-3283-2_7en
dc.subjectInfectionen
dc.subjectMathematicalen
dc.subjectModelingen
dc.subjectEpidemicen
dc.subjectTuberculosisen
dc.subjectHIVen
dc.subjectR Medicineen
dc.subject.lccRen
dc.titleSystems medicine and infectionen
dc.typeBook itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.Gillespie Groupen
dc.identifier.doihttps://doi.org/10.1007/978-1-4939-3283-2_7
dc.date.embargoedUntil2017-01-03


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