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

EAGER：多尺度模型的模块化设计，应用于对真菌呼吸道病原体的先天免疫反应

• 批准号: 1750183
• 负责人: Reinhard Laubenbacher
• 金额: $ 9万
• 依托单位: University of Connecticut Health Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750183&HistoricalAwards=false
• 关键词: EAGER; Modular; design; multiscale; models

The wealth of biomedical data available today -- often with wide ranging size and time scales -- allows for the validation and calibration of complex computational models that integrate across levels from molecules to whole organisms. For those models to be usable by scientists and clinicians who are experts in the biomedical phenomenon being modeled, it is important that the model features and activity be presented visually to make them understandable. This project seeks to develop a novel, modular computational approach to this challenge. It will use modeling of the immune response to an important respiratory fungal infection as a test bed to demonstrate the feasibility and effectiveness of this approach. This project will advance knowledge in computational modeling of biomedical systems and in the pathophysiology of infection. This particular infection has become increasingly relevant as it occurs most frequently in immuno-compromised individuals, including cancer and transplant patients. The developed model will examine how an individual's immune system interacts with the fungal spores to better understand the progression of this infection. The eventual goal would be to take advantage of this improved understanding to spur the development of new treatments for this fungal infection.This complexity of multiscale models of biomedical processes poses multiple challenges related to mathematical modeling, software design, validation, reproducibility, and extensibility. The computational goal of this project is to develop a novel modular approach to model architecture, using a recently introduced technology of lightweight virtual machines and a 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. For this project, this computational modeling will be focused on the development of a multiscale model capturing the early stages of invasive aspergillosis. 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. The biomedical 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.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

如今，丰富的生物医学数据——通常具有广泛的规模和时间尺度——允许对复杂的计算模型进行验证和校准，这些模型集成了从分子到整个生物体的各个层面。为了使这些模型能够被作为生物医学现象建模专家的科学家和临床医生使用，重要的是将模型的特征和活动以可视化的方式呈现出来，使它们易于理解。该项目旨在开发一种新颖的模块化计算方法来应对这一挑战。它将使用对一种重要的呼吸道真菌感染的免疫反应建模作为测试平台来证明这种方法的可行性和有效性。该项目将促进生物医学系统的计算建模和感染的病理生理学方面的知识。这种特殊的感染已经变得越来越重要，因为它最常发生在免疫受损的个体中，包括癌症和移植患者。开发的模型将检查个体的免疫系统如何与真菌孢子相互作用，以更好地了解这种感染的进展。最终的目标是利用这一改进的认识来促进这种真菌感染的新治疗方法的发展。生物医学过程的多尺度模型的复杂性带来了与数学建模、软件设计、验证、可重复性和可扩展性相关的多重挑战。该项目的计算目标是开发一种新颖的模块化方法来建模架构，使用最近引入的轻量级虚拟机技术和一个用户友好的开源平台来构建和链接这些所谓的“Docker容器”，以透明的方式创建复杂的模块化模型。软件容器的一个关键优点是它们可以包含模型的整个计算环境，从而实现计算结果的前所未有的再现性。对于这个项目，这个计算模型将侧重于开发一个多尺度模型，捕捉侵袭性曲霉病的早期阶段。侵袭性曲霉病是免疫功能低下宿主中最常见的真菌感染之一，预后较差。致病菌烟曲霉（Aspergillus fumigatus）孢子在环境中无处不在。健康的宿主清除吸入的孢子而不会发病，但免疫力受损的个体容易受到威胁生命的呼吸道感染，然后传播到其他器官。在移植和癌症中越来越多地使用免疫抑制疗法，大大增加了这种感染的痛苦和死亡，预计这一趋势将继续下去。拟议项目的生物医学重点是真菌和宿主之间对铁的争夺。总体生物医学目标是开发一种模拟工具来探索铁在侵袭性曲霉病中的生物化学和生物物理条件下的作用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。