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Systems medicine and infection

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Abstract

By 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 .
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Details

Original languageEnglish
Title of host publicationSystems Medicine
EditorsUlf Schmitz, Olaf Wolkenhauer
PublisherSpringer
Pages107-118
ISBN (Electronic)9781493932832
ISBN (Print)9781493932825
DOIs
StatePublished - 2016

Publication series

NameMethods in Molecular Biology
PublisherHumana Press
Volume1386
ISSN (Print)1064-3745

    Research areas

  • Infection, Mathematical, Modeling, Epidemic, Tuberculosis, HIV

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