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

描述(申请人提供)：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。尽管经过了几十年的研究，但我们对各种病原体和免疫过程的相互作用--导致结核病(TB)不同结局--即原发结核病、潜伏期结核病和再激活结核病--的了解仍然不完整。结核病的特点是在肺和淋巴结中形成一个球形的免疫细胞集合，这些细胞在免疫上抑制并在物理上遏制细菌。然而，杆菌可以在肉芽肿中存活数年。目前的治疗方法需要使用多种抗生素进行6个月的治疗；免疫调节可能能够加强这种治疗，缩短治疗时间，减少副作用。迫切需要一种In Silico平台来提供一种预测新治疗策略结果的经济有效的手段。该项目的长期目标是将这些器官中免疫系统动力学的知识整合到一个现实的、多尺度的、多器官的结核杆菌感染期间免疫反应的模型中，并使用该模型来确定免疫调节/抗生素治疗的最佳方法。具体目标是：目标1：将新的成分(IL-10，细菌种群动态)引入我们现有的多尺度肺肉芽肿模型，并使用该模型预测影响肺部感染控制的因素。目的2：将新的信息(淋巴结解剖、关键细胞因子和细菌种群)融入到我们现有的多尺度淋巴结模型中，并使用该模型来预测导致淋巴结内启动免疫反应和肉芽肿形成和维持的因素。目的3.建立包括AIMS 1和AIMS 2模型以及器官间转运事件的多室多尺度模型，并利用该模型在免疫调节/抗生素治疗过程中在单个肉芽肿水平上预测感染控制和病理。这里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--结合了计算和实验工具--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，为更广泛的研究人员社区提供服务，这些研究人员正在研究与结核病、免疫和多尺度建模相关的领域。 公共卫生相关性：使用实验/计算系统生物学相结合的方法，我们将开发一个现实的多尺度多隔室模型，描述对感染导致结核病的细菌的免疫反应。该模型将用于预测在抗生素治疗期间提高免疫力的治疗策略的结果，为评估治疗干预措施提供一种具有成本效益的手段。

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. PUBLIC HEALTH RELEVANCE: Using a combined experimental/computational systems biology approach, we will develop a realistic multi-scale multi-compartment model that describes the immune response to infection with the bacteria that causes tuberculosis. The model will be used to predict the outcome of treatment strategies that boost immunity during antibiotic treatment, providing a cost-effective means of evaluating therapeutic interventions.