In silico modelling of in-host tuberculosis dynamics : towards building the virtual patient
Abstract
Tuberculosis (TB) accounts for over 1 million deaths each year, despite
effective treatment regimens being available. Improving the treatment of
TB will require new regimens, each of which will need to be put through
expensive and lengthy clinical trials, with no guarantee of success. The
ability to predict which of many novel regimens to progress through the
clinical trial stages would be an important tool to TB research. as current
models are constrained in their ability to reflect the whole spectrum of
pathophysiology, particularly as there remains uncertainty around the
events that occur.
This thesis explores the use of computational techniques to model a
pulmonary human TB infection. We introduce the first in silico model
of TB occurring over the whole lung that incorporates both the environmental heterogeneity that is exhibited within different spatial regions of
the organ, and the different bacterial dissemination routes, in order to
understand how bacteria move during infection and why post-primary
disease is typically localised towards the apices of the lung.
Our results show that including environmental heterogeneity within
the lung can have profound effects on the results of an infection, by
creating a region towards the apex which is preferential for bacterial
proliferation. We also present a further iteration of the model, whereby
the environment is made more granular to better understand the regions
which are afflicted during infection, and show how sensitivity analysis
can determine the factors that contribute most to disease outcomes.
We show that in order to simulate TB disease within a human lung
with sufficient accuracy, better understanding of the dynamics is required.
The model presented in this thesis is intended to provide insight into
these complicated dynamics, and thus make progress towards an end
goal of a virtual clinical trial, consisting of a heterogeneous population of
synthetic virtual patients.
Type
Thesis, PhD Doctor of Philosophy
Rights
Creative Commons Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
Collections
Description of related resources
Pitcher, M. J., Bowness, R., Dobson, S., & Gillespie, S. H. (2018). A spatially heterogeneous network-based metapopulation software model applied to the simulation of a pulmonary tuberculosis infection. Applied Network Science, 3(1). https://doi.org/10.1007/s41109-018-0091-2Pitcher, M. J., Bowness, R., Dobson, S., Eftimie, R., & Gillespie, S. H. (2019). Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle. BioRxiv. https://doi.org/10.1101/2019.12.12.871269
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