Biological Models of Influenza A Virus

描述（由申请人提供）：本提案的目的是开发原发性流感感染或继发性肺炎引起的肺损伤的多尺度模型，并从模拟中确定非平凡情况下的最佳干预策略，例如高毒力病原体和次优免疫。这项工作将包括开发宿主-流感相互作用的简化和更详细的模型，先天性和适应性免疫反应以及肺损伤下的肺部气体交换。这些模型将用来自感染不同毒力流感病毒株的小鼠的一系列实验的前瞻性数据进行校准，有或没有免疫接种，有或没有抗病毒药物治疗。 这组跨学科的研究人员拥有多年开发感染和炎症的理论和动物模型以及先前合作的记录。完成后，该提案将实现三个重要的科学目标：（1）为设计基于个体和群体的高致病力流感遏制策略时所用的假设提供坚实的生物学基础，（2）为进一步研究包括器官衰竭机制在内的多尺度全器官模型提供基础;（3）提供定量方法来评估与模型预测相关的不确定性，当这些模型是从不完善或稀疏的经验数据校准时。 拟议的活动将涉及在一个致力于向临床医生、生物学家和定量科学家传播科学的跨学科环境中培训博士后研究员、研究生和本科生。这一提议将确认这些调查人员继续坚定地致力于培训女科学家，并将接触到代表性不足的少数群体，特别是在本科一级。该项目还将加强现有资源，如学员和有成就的科学家广泛使用的XPP免费软件、网络可访问资源（模型和软件）以及基于网络的数据储存库。 这组研究人员认为，数学建模和计算是将大量数据转化为知识的重要工具，这将有利于患者护理和社会对潜在灾难性新兴传染病威胁的准备。

DESCRIPTION (provided by applicant): The objectives of this proposal are to develop multiscale models of lung injury caused by either primary Influenza infection or secondary pneumonia, and identify from simulations optimal intervention strategies in non-trivial situations, such as highly virulent pathogens and sub-optimal immunization. This effort will include the development of reduced and more detailed models of the host-influenza interaction, the innate and adaptive immune response and pulmonary gas exchange under evolving lung injury. These models will be calibrated with prospective data from a series of experiment of mice infected with strains of Influenza of varying virulence, with or without immunization, and with or without antiviral pharmacotherapy. This group of interdisciplinary investigators has a track record of several years of developing theoretical and animal models of infection and inflammation, and of prior collaboration. When completed, this proposal will have accomplished three important scientific goals: (1) provide a solid biological basis for assumptions used in the design of individual and population-based containment strategies of highly virulent Influenza, (2) provide a foundation for further studies of multiscale whole organ models including mechanisms of organ failure and (3) provide quantitative methods to assess the uncertainty associated with model predictions when such models are calibrated from imperfect or sparse empirical data. The proposed activities will involve the training post-doctoral fellows, graduate and undergraduate students in an interdisciplinary environment strongly dedicated to scientific dissemination to clinicians, biologists and quantitative scientists. This proposal will confirm these investigators strong continued commitment to the training of women scientists, and will reach out to underrepresented minorities, particularly at the undergraduate level. This project will also enhance existing resources such as the XPP freeware, widely used by trainees and accomplished scientists, web accessible resource (models and software), and web based data repositories. It is the belief of this group of investigator that mathematical modeling and computation are essential tools in translating large streams of data in knowledge that will benefit patient care and societal preparedness to potentially catastrophic emerging infectious threats.