How mechanistic in silico modelling can improve our understanding of TB disease and treatment

Michael Pitcher; Simon Dobson; Tom Kelsey; Mark Chaplain; Derek Sloan; Stephen H. Gillespie; Ruth Bowness

doi:10.5588/ijtld.20.0107

How mechanistic in silico modelling can improve our understanding of TB disease and treatment

Michael Pitcher, Simon Dobson, Tom Kelsey, Mark Chaplain, Derek Sloan, Stephen H. Gillespie, Ruth Bowness

Department of Mathematical Sciences

Research output: Contribution to journal › Article › peer-review

4 Citations (SciVal)

Abstract

TB is one of the top 10 causes of death worldwide and the leading cause of death from a single infectious agent. Decreasing the length of time for TB treatment is an important step towards the goal of reducing mortality. Mechanistic in silico modelling can provide us with the tools to explore gaps in our knowledge, with the opportunity to model the complicated within-host dynamics of the infection, and simulate new treatment strategies. Significant insight has been gained using this form of modelling when applied to other diseases – much can be learned in infection research from these advances.

Original language	English
Pages (from-to)	1145-1150
Number of pages	6
Journal	The International Journal of Tuberculosis and Lung Disease
Volume	24
Issue number	11
DOIs	https://doi.org/10.5588/ijtld.20.0107
Publication status	Published - 1 Nov 2020

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10.5588/ijtld.20.0107Licence: CC BY

A novel hybrid discrete-continum cellelar autoaton model to study tuberculosis disease progression and treatment
Bowness, R. (PI)
MRC
15/09/20 → 31/10/23
Project: Research council

Cite this

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abstract = "TB is one of the top 10 causes of death worldwide and the leading cause of death from a single infectious agent. Decreasing the length of time for TB treatment is an important step towards the goal of reducing mortality. Mechanistic in silico modelling can provide us with the tools to explore gaps in our knowledge, with the opportunity to model the complicated within-host dynamics of the infection, and simulate new treatment strategies. Significant insight has been gained using this form of modelling when applied to other diseases – much can be learned in infection research from these advances.",

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AU - Sloan, Derek

AU - Gillespie, Stephen H.

AU - Bowness, Ruth

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DO - 10.5588/ijtld.20.0107

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How mechanistic in silico modelling can improve our understanding of TB disease and treatment

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A novel hybrid discrete-continum cellelar autoaton model to study tuberculosis disease progression and treatment

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