Biological Models of Influenza A Virus

描述(申请人提供)：这项建议的目标是发展由原发流感感染或继发性肺炎引起的多尺度肺损伤模型，并从模拟中确定在非平凡情况下的最佳干预策略，如高毒力病原体和次优免疫。这项工作将包括开发更少和更详细的宿主-流感相互作用、先天和获得性免疫反应以及在不断演变的肺损伤下的肺气体交换的模型。这些模型将根据一系列实验的预期数据进行校准，这些实验针对感染了不同毒力的流感病毒株的小鼠，无论是否进行免疫，以及是否进行抗病毒药物治疗。这组跨学科的研究人员有几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，这项建议将实现三个重要的科学目标：(1)为设计基于个体和基于群体的高毒力流感遏制策略所使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整体器官模型奠定基础，以及(3)提供量化方法，以评估从不完善或稀疏的经验数据校准模型时与模型预测相关的不确定性。拟议的活动将涉及在致力于向临床医生、生物学家和量化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。这项提议将证实这些调查人员坚定地继续致力于培训女科学家，并将接触到代表性不足的少数群体，特别是在本科水平。该项目还将加强现有资源，如被受训人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)和基于网络的数据储存库。这组研究人员相信，数学建模和计算是转化知识中大量数据流的基本工具，这些数据流将有助于患者护理和社会对潜在灾难性新出现的传染病威胁的准备。

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.