A multi-scale model to predict outcomes of immunomodulation and drug therapy duri

描述（由申请人提供）：用于预测结核病期间免疫调节和药物治疗结果的多尺度模型结核分枝杆菌（Mtb）是人类已知的最成功的病原体；它每年造成约200万人死亡，估计感染了世界三分之一的人口。尽管进行了数十年的研究，但我们对各种病原体和免疫过程之间的相互作用的理解仍然不完整，这些相互作用导致结核病的不同结果，即原发性结核病、潜伏性结核病和再激活性结核病。结核病的标志是在肺和淋巴结中形成免疫细胞的球形集合，既在免疫上抑制细菌，又在物理上遏制细菌。然而杆菌可以在肉芽肿中存活数年。目前的治疗需要6个月的多种抗生素治疗；免疫调节可以增强这种治疗，缩短治疗时间并减少副作用。迫切需要一种计算机平台，以提供一种具有成本效益的方法来预测新治疗策略的结果。该项目的长期目标是将这些器官的免疫系统动力学知识整合到结核分枝杆菌感染期间免疫反应的现实，多尺度，多器官模型中，并使用该模型确定免疫调节/抗生素治疗的最佳方法。目标1：将新的成分（IL-10，细菌种群动态）纳入我们现有的多尺度肺肉芽肿模型，并利用该模型预测影响肺部感染控制的因素。目标2：将新的信息（淋巴结解剖，关键细胞因子和细菌种群）纳入我们现有的多尺度淋巴结模型，并使用该模型预测导致免疫反应启动和肉芽肿形成和维持的因素。目标3。建立包括Aims 1和Aims 2模型以及器官间转运事件模型的多室、多尺度模型，并利用该模型预测免疫调节/抗生素治疗期间单个肉芽肿水平的感染控制和病理。本文从非人类灵长类动物中产生的数据将为我们的模型提供信息，并用于验证预测。我们的系统生物学方法——结合计算和实验工具——将使我们能够预测和测试有关影响结核病免疫力的关键机制的假设。我们的跨学科方法还将通过提供随时可用的数据和工具，为调查结核病、免疫和多尺度建模相关领域的更广泛的研究人员社区提供服务。

DESCRIPTION (provided by applicant): A multi-scale model to predict outcomes of immunomodulation and drug therapy during tuberculosis Mycobacterium tuberculosis (Mtb) is the most successful pathogen known to humans; it is responsible for ~2 million deaths/year and infects an estimated 1/3 of the world. Despite decades of study, our understanding of the interplay of various pathogen and immune processes that allow for different outcomes in tuberculosis (TB), i.e. primary TB, latency and reactivation TB, remains incomplete. The hallmark of TB is the formation of a spherical collection of immune cells in the lung and lymph node that both immunologically restrains and physically contains the bacteria. Yet bacilli can survive within granuloma for years. Current therapy requires 6 months of treatment with multiple antibiotics; immunomodulation may be able to augment this treatment, shortening treatment time and reducing side effects. There is a crucial need for an in silico platform to provide a cost-effective means of predicting the outcome of new treatment strategies. The long-term goals of this project are to integrate knowledge about immune system dynamics in these organs into a realistic, multi-scale, multi-organ model of the immune response during Mtb infection and to use this model to identify optimal approaches for immunomodulation/antibiotic therapy. The specific aims are: Aim 1: Incorporate new components (IL-10, bacterial population dynamics) into our existing multi-scale lung granuloma model, and use the model to predict factors affecting control of infection in the lung. Aim 2: Incorporate new information (lymph node anatomy, key cytokines, and bacterial populations) into our existing multi-scale lymph node model, and use the model to predict factors leading to initiation of the immune response and granuloma formation and maintenance in a lymph node. Aim 3. Build a multi-compartment, multi-scale model that includes the models of Aims 1 and 2 and trafficking events between the organs, and use this model to predict infection control and pathology at the level of individual granulomas during immunodulation/antibiotic therapy. Data generated herein from non-human primates will inform our models and be used to validate predictions. Our systems biology approach - incorporating both computational and experimental tools - will allow us to predict and test hypotheses regarding key mechanisms that influence immunity to TB. Our interdisciplinary approach will also serve the broader community of researchers investigating areas related to TB, immunity and multi-scale modeling by providing data and tools that will be made readily available.