A novel hybrid discrete-continuum cellular automaton model to study tuberculosis disease progression and treatment

一种用于研究结核病进展和治疗的新型混合离散连续元细胞自动机模型

基本信息

批准号：
MR/P014704/1
负责人：
Ruth Bowness
金额：
$ 33.19万
依托单位：
University of St Andrews
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FP014704%2F1
关键词：
novel hybrid discrete continuum cellular

项目摘要

Tuberculosis (TB) is an infection caused by a bacterium, M tuberculosis. It is the biggest infectious killer globally, with a person dying from the disease every twenty seconds. Treatment length urgently needs to be reduced in order to aid compliance to therapy, reducing emergence of antibiotic resistance. Until more can be learnt about the disease pathology and how drugs behave in the lung, however, treatment will remain at six months. New drugs are crucial to permit elimination of the disease, but clinical trials are expensive and long, and not all of the possible new regimens can be evaluated rapidly. This research will use mathematical modelling to assist in the fight against tuberculosis. Model simulations can potentially be used to accelerate the rate of discovery, while reducing the need for expensive lab work and clinical trials. These models are driven by observations and are based on our understanding of the question at hand. They generate specific, explicitly testable predictions that can be proved by experiment. Previous tuberculosis models have quantified treatment response in clinical trials by analysing patients' sputum samples during treatment. A mathematical model that captures disease more accurately will enable better predications to be made.When TB bacteria enter the lungs, the immune system attempts to control the disease, resulting in a localised reaction: a granuloma. When granulomas are unable to contain the bacteria, active disease develops. After diagnosis, patients are given a combination of antibiotics for a minimum of six months. How well the standard treatments penetrate into the granulomas or how well bacteria respond to the mix of antibiotics will define what the outcome of treatment will be.I have developed a model to study tuberculosis disease progression and treatment in the lung. The model describes, using numbers, the movement and interactions of bacteria and immune cells in both time and space. My research plan outlines how I will enhance this model: by completing comprehensive training with collaborating experimentalists, mathematicians and computer scientists, I will develop the skills and knowledge required to consolidate my ability to develop the model. Collaboration with identified key individuals whose research focuses on the penetration of tuberculosis antibiotics into granulomas is the first vital step in our model development. Alongside this, we will incorporate data from a laboratory simulator that mimics changes in drug concentration over time, as they would occur in humans. The system allows multiple combinations of drugs to be integrated into our model. Researchers at the University of Michigan have a well-established model called 'GranSim'. Although they have a different focus to their work, their model simulates granuloma formation in TB infection and both the modelling and the immunology knowledge I would gain from spending time in their research group would be hugely beneficial for this project.Finally, in collaboration with the computer scientists at the University, I plan to extend our mathematical model to 3D. Using various visualization techniques, we will be able to view the model simulations in a more understandable way, and features that were impossible in 2D will be seen. It might be possible to display the model on a 360 degree screen enabling the complex activities going on in the depth of the lung to be seen and the detail understood. My PhD student will develop this work further to create a model which follows the interaction of the granuloma in the wider lung: a key step along the path to a virtual patient.Thus, our proposed model developments will allow us to answer some of the complex questions that underlie poor treatment response and relapse in TB. My innovative research approach integrates clinical and experimental results with mathematical techniques to address the problem of shortening tuberculosis treatment.

结核病（TB）是由细菌，结核病引起的感染。它是全球最大的传染性杀手，一个人每二十秒死于该疾病。需要迫切需要减少治疗长度，以帮助遵守治疗，从而降低抗生素耐药性的出现。在可以了解疾病病理学以及药物如何在肺中行为的更多信息之前，治疗将在六个月内保留。新药对于消除疾病至关重要，但是临床试验既昂贵又长，并且并非所有可能的新方案都可以迅速评估。这项研究将使用数学建模来协助对抗结核病。模型模拟可以可能用于加速发现率，同时减少对昂贵的实验室工作和临床试验的需求。这些模型是由观察驱动的，是基于我们对当前问题的理解。它们产生特定的，明确的测试预测，可以通过实验证明。先前的结核病模型通过分析治疗过程中患者的痰液样本来量化临床试验中的治疗反应。一个数学模型更准确地捕获疾病将使能够更好地鉴定。当结核病细菌进入肺部时，免疫系统试图控制该疾病，从而导致局部反应：肉芽肿。当肉芽瘤无法遏制细菌时，会出现活性疾病。诊断后，给予患者至少六个月的抗生素组合。标准治疗方法如何渗透到颗粒中，或细菌对抗生素的混合的反应如何定义治疗的结果。我已经开发了一种研究结核病疾病进展和肺部治疗的模型。该模型使用数字描述了在时间和空间中细菌和免疫细胞的运动和相互作用。我的研究计划概述了我将如何增强该模型：通过与合作的实验者，数学家和计算机科学家完成全面的培训，我将发展所需的技能和知识，以巩固我开发该模型的能力。与确定的关键人物的合作，其研究重点是将结核病抗生素渗透到肉芽肿中，这是我们模型开发的第一步。除此之外，我们将合并来自实验室模拟器的数据，该数据随着时间的流逝而模仿药物浓度的变化，就像它们在人类中一样。该系统允许将多种药物组合整合到我们的模型中。密歇根大学的研究人员有一个公认的模型，称为“ Gransim”。尽管他们对工作有不同的重点，但他们的模型模拟了结核病感染中的肉芽肿形成，以及我从在他们的研究小组中度过的时间将获得的建模和免疫学知识将对该项目产生极大的好处。从本文中，我计划与大学的计算机科学家合作，我计划将我们的数学模型扩展到3D。使用各种可视化技术，我们将能够以更易于理解的方式查看模型模拟，并且将看到2D中不可能的功能。可能可以在360度屏幕上显示该模型，从而可以在肺深处进行复杂的活动，并了解细节。我的博士生将进一步开发这项工作，以创建一个模型，该模型遵循较宽的肺部肉芽肿的相互作用：沿通往虚拟患者的路径的关键步骤。因此，我们提出的模型开发将使我们能够回答一些复杂的问题，这些问题是治疗差不良反应和TB中复发的基础。我的创新研究方法将临床和实验结果与数学技术相结合，以解决缩短结核病治疗的问题。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: Insights from a hybrid multiscale cellular automaton model.

DOI：
10.1016/j.jtbi.2018.03.006
发表时间：
2018-06-07
期刊：
Journal of theoretical biology
影响因子：
2
作者：
Bowness R;Chaplain MAJ;Powathil GG;Gillespie SH
通讯作者：
Gillespie SH

Mathematical methods for scaling from within-host to population-scale in infectious disease systems

DOI：
10.1016/j.epidem.2023.100724
发表时间：
2023-11-17
期刊：
EPIDEMICS
影响因子：
3.8
作者：
Doran，James W. G.;Thompson，Robin N.;Bowness，Ruth
通讯作者：
Bowness，Ruth

A simple label-free method reveals bacterial growth dynamics and antibiotic action in real-time.

DOI：
10.1038/s41598-022-22671-6
发表时间：
2022-11-12
期刊：
SCIENTIFIC REPORTS
影响因子：
4.6
作者：
Hammond, Robert J. H.;Falconer, Kerry;Powell, Thomas;Bowness, Ruth;Gillespie, Stephen H.
通讯作者：
Gillespie, Stephen H.

A hybrid individual-based mathematical model to study bladder infections

研究膀胱感染的基于个体的混合数学模型