How mechanistic in silico modelling can improve our understanding of TB disease and treatment
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
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.
Pitcher , M J , Dobson , S A , Kelsey , T , Chaplain , M A J , Sloan , D J , Gillespie , S H & Bowness , R 2020 , ' How mechanistic in silico modelling can improve our understanding of TB disease and treatment ' , International Journal of Tuberculosis and Lung Disease , vol. 24 , no. 11 , pp. 1145-1150 . https://doi.org/10.5588/ijtld.20.0107
International Journal of Tuberculosis and Lung Disease
Copyright © 2020 Pitcher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DescriptionFunding: Medical Research Council [grant number MR/P014704/1], The Academy of Medical Sciences (AMS), the Wellcome Trust, the Government Department of Business, Energy and Industrial Strategy (BEIS), the British Heart Foundation and the Global Challenges Research Fund (GCRF) [grant number SBF003\1052] and 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.
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Showing items related by title, author, creator and subject.
Computational modelling of cancer development and growth : modelling at multiple scales and multiscale modelling Szymanska, Zuzanna; Cytowski, Maciej; Mitchell, Elaine; Macnamara, Cicely K.; Chaplain, Mark A. J. (2018-05) - Journal articleIn this paper, we present two mathematical models related to different aspects and scales of cancer growth. The first model is a stochastic spatiotemporal model of both a synthetic gene regulatory network (the example of ...
Meedeniya, Dulani Apeksha (University of St Andrews, 2013-06-26) - ThesisModern software systems have increasingly higher expectations on their reliability, in particular if the systems are critical and real-time. The development of these complex software systems requires strong modelling and ...
Incorporating Model Uncertainty into the Sequential Importance Sampling Framework using a Model Averaging Approach, or Trans-Dimensional Sequential Importance Sampling. Lynam, Christopher; King, Ruth; Thomas, Len; Buckland, Stephen T. (CREEM, University of St Andrews, 2007) - ReportA sequential Bayesian Monte Carlo approach is proposed in which model space can be explored during the Sequential Importance Sampling (SIS, a.k.a. Particle Filtering) fitting process. The algorithm allows model space to ...