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Systems medicine and infection

Research output: Chapter in Book/Report/Conference proceedingChapter

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Systems medicine and infection. / Bowness, Ruth.

Systems Medicine. ed. / Ulf Schmitz; Olaf Wolkenhauer. Springer, 2016. p. 107-118 (Methods in Molecular Biology; Vol. 1386).

Research output: Chapter in Book/Report/Conference proceedingChapter

Harvard

Bowness, 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_7

APA

Bowness, R. (2016). Systems medicine and infection. In U. Schmitz, & O. Wolkenhauer (Eds.), Systems Medicine (pp. 107-118). (Methods in Molecular Biology; Vol. 1386). Springer. https://doi.org/10.1007/978-1-4939-3283-2_7

Vancouver

Bowness R. Systems medicine and infection. In Schmitz U, Wolkenhauer O, editors, Systems Medicine. Springer. 2016. p. 107-118. (Methods in Molecular Biology). https://doi.org/10.1007/978-1-4939-3283-2_7

Author

Bowness, Ruth. / Systems medicine and infection. Systems Medicine. editor / Ulf Schmitz ; Olaf Wolkenhauer. Springer, 2016. pp. 107-118 (Methods in Molecular Biology).

Bibtex - Download

@inbook{c39ed1f6f88c4ea493ac9bdf1d431ff1,
title = "Systems medicine and infection",
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 .",
keywords = "Infection, Mathematical, Modeling, Epidemic, Tuberculosis, HIV",
author = "Ruth Bowness",
year = "2016",
doi = "10.1007/978-1-4939-3283-2_7",
language = "English",
isbn = "9781493932825",
series = "Methods in Molecular Biology",
publisher = "Springer",
pages = "107--118",
editor = "Ulf Schmitz and Olaf Wolkenhauer",
booktitle = "Systems Medicine",
address = "Netherlands",

}

RIS (suitable for import to EndNote) - Download

TY - CHAP

T1 - Systems medicine and infection

AU - Bowness, Ruth

PY - 2016

Y1 - 2016

N2 - 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 .

AB - 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 .

KW - Infection

KW - Mathematical

KW - Modeling

KW - Epidemic

KW - Tuberculosis

KW - HIV

U2 - 10.1007/978-1-4939-3283-2_7

DO - 10.1007/978-1-4939-3283-2_7

M3 - Chapter

SN - 9781493932825

T3 - Methods in Molecular Biology

SP - 107

EP - 118

BT - Systems Medicine

A2 - Schmitz, Ulf

A2 - Wolkenhauer, Olaf

PB - Springer

ER -

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ID: 229012001