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

结核病是一种由结核分枝杆菌引起的感染。它是全球最大的传染病杀手，每20秒就有一人死于这种疾病。迫切需要缩短治疗时间，以帮助治疗的依从性，减少抗生素耐药性的出现。然而，在对这种疾病的病理和药物对肺部的作用有更多了解之前，治疗将停留在6个月。新药对于消灭这种疾病至关重要，但临床试验既昂贵又漫长，而且并非所有可能的新方案都能迅速得到评估。这项研究将使用数学模型来帮助抗击结核病。模型模拟可以潜在地用于加快发现速度，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，并基于我们对手头问题的理解。它们产生具体的、明确的、可测试的预测，这些预测可以通过实验证明。以前的结核病模型在临床试验中通过分析患者治疗期间的痰样本来量化治疗反应。更准确地捕捉疾病的数学模型将有助于做出更好的预测。当结核细菌进入肺部时，免疫系统试图控制疾病，导致局部反应：肉芽肿。当肉芽肿无法控制细菌时，活动性疾病就会发展。在确诊后，患者会接受至少6个月的抗生素联合治疗。标准治疗方法对肉芽肿的渗透程度或细菌对抗生素混合物的反应程度将决定治疗的结果。我开发了一个模型来研究肺病的进展和治疗。该模型用数字描述了细菌和免疫细胞在时间和空间上的运动和相互作用。我的研究计划概述了我将如何增强这一模型：通过完成与合作实验者、数学家和计算机科学家的全面培训，我将发展所需的技能和知识，以巩固我开发模型的能力。与研究结核抗生素进入肉芽肿的关键人物合作是我们模型开发的第一步。除此之外，我们将结合实验室模拟器的数据，模拟药物浓度随时间的变化，就像它们会发生在人类身上一样。该系统允许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完善的模型，叫做“GranSim”。虽然他们的工作重点不同，但他们的模型模拟了结核病感染中的肉芽肿形成，我将从他们的研究小组中获得建模和免疫学知识，这对这个项目非常有益。最后，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，我们将能够以更容易理解的方式查看模型模拟，并且可以看到2D中不可能出现的特征。在360度的屏幕上显示模型是可能的，这样就可以看到肺深处发生的复杂活动，并了解细节。我的博士生将进一步发展这项工作，以创建一个模型，该模型遵循更广泛肺部肉芽肿的相互作用：这是通往虚拟患者的关键一步。因此，我们提出的模型开发将使我们能够回答导致结核病治疗反应差和复发的一些复杂问题。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。

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.