Modular design of multiscale models, with an application to the innate immune response to fungal respiratory pathogens.

项目摘要 跨空间和时间尺度的生物医学数据集的增加使得有可能校准 捕捉从分子到整个有机体水平的综合过程的复杂模型。这 复杂性提出了与数学建模、软件设计、验证 可重复性和可扩展性。模型特征和动态的可视化是 领域专家的模型，如实验生物学家和临床医生。拟议项目涉及 这些挑战的背景下，免疫反应的一个重要的呼吸道真菌感染。它的目标是 开发一种新的模块化方法来建模架构，使用最近推出的技术， 轻量级的虚拟机和我们的用户友好的开源平台，用于构建和连接这些 所谓的“Docker容器”，以透明的方式创建复杂的模块化模型。的关键益处 软件容器的另一个优点是它们可以包含模型的整个计算环境， 前所未有的计算结果再现性。首要的计算目标是开发一个 多尺度模型模块化设计的新方法。虽然广泛适用，但这种新的计算 建模方法将侧重于开发一个多尺度模型，捕捉早期阶段的 侵袭性曲霉病，一个重要的健康问题。 侵袭性曲霉病是免疫受损宿主中最常见的真菌感染之一， 预后很差致病微生物烟曲霉的孢子， 分布在环境中。健康的宿主会清除吸入的孢子而不产生疾病， 免疫力受损的个体容易受到威胁生命的呼吸道感染， 传播到其他器官。免疫抑制疗法在移植中的使用越来越多， 癌症已经大大增加了这种感染的痛苦和死亡，预计这一趋势将 继续目前的治疗方法主要集中在病原体上，但更好的方法是 了解这种感染中宿主防御的组成部分可能会导致新的 治疗。特别地，限制铁的可用性是抗微生物宿主防御的关键机制; 相反，成功的病原体已经进化出从宿主体内清除铁的有效机制。这些 这些机制有可能被用于治疗。拟议项目的生物重点是 真菌和宿主之间争夺铁的斗争生物医学的首要目标是开发一种 一种模拟工具，用于探索铁在生物化学和生物物理侵袭性曲霉病中的作用 条件

PROJECT SUMMARY Increased availability of biomedical data sets across spatial and temporal scales makes it possible to calibrate complex models that capture integrated processes from the molecular to the whole organism level. This complexity poses multiple challenges related to mathematical modeling, software design, validation, reproducibility, and extensibility. Visualization of model features and dynamics is a key factor in the usability of models by domain experts, such as experimental biologists and clinicians. The proposed project addresses these challenges in the context of the immune response to an important respiratory fungal infection. Its goal is to develop a novel modular approach to model architecture, using a recently introduced technology of lightweight virtual machines and our user-friendly open-source platform for the construction and linking of these so-called “Docker containers” to create complex modular models in a transparent fashion. A key benefit of software containers is that they can encompass the entire computational environment of a model, enabling unprecedented reproducibility of computational results. The overarching computational goal is to develop a novel approach to the modular design of multiscale models. While broadly applicable, this novel computational modeling approach will be focused on the development of a multiscale model capturing the early stages of invasive aspergillosis, an important health problem. Invasive aspergillosis is one of the most common fungal infections in immunocompromised hosts and carries a poor prognosis. The spores of the causative organism, Aspergillus fumigatus, are ubiquitously distributed in the environment. Healthy hosts clear the inhaled spores without developing disease, but individuals with impaired immunity are susceptible to a life-threatening respiratory infection that can then disseminate to other organs. The increasing use of immunosuppressive therapies in transplantation and cancer has dramatically increased suffering and death from this infection, and this trend is expected to continue. Current therapeutic approaches have been focused primarily on the pathogen, but a better understanding of the components of host defense in this infection may lead to the development of new treatments. In particular, restricting iron availability is a critical mechanism of antimicrobial host defense; conversely, successful pathogens have evolved potent mechanisms for scavenging iron from the host. These mechanisms have the potential to be harnessed therapeutically. The biological focus of the proposed project is the battle over iron between the fungus and the host. The overarching biomedical goal is to develop a simulation tool to explore the role of iron in invasive aspergillosis across biochemical and biophysical conditions.