Predictive Biosignatures for Complicated Novel H1N1 Influenza

描述(由申请人提供)：这项提案将首次加入先进的计算算法、多尺度数学模型、最先进的成像和生物测量、高度相关的动物模型和现有的人类数据，以解决与人类健康迫在眉睫的翻译问题：甲型流感大流行病毒感染(IAV)及其可能导致大量个人的危重疾病和死亡。事实上，2009年H1N1流感重组病毒迄今估计造成18,000人死亡，目标是年轻人和孕妇，在入住重症监护病房的近一半病例中造成急性肺损伤，并与高达35%的这些病例中的继发性细菌感染有关。2009年甲型H1N1流感不仅本身具有重大意义，预计在2010年秋季至少会出现第三波，而且它代表着一种大流行比例的典型新发传染病。因此，它提供了一个难得的机会来加深我们对以下方面的理解：(1)与甲型流感病毒致病性有关的机制；(2)严重和复杂细菌感染的预后生物标志物；以及(3)利用物理科学的工具为下一次“大灾难”加强知识和准备工作的潜在贡献。我们建议(1)在包括活体成像在内的多个水平上对细胞、细胞间过程和器官功能的实时观察进行建模，(2)开发严重并发症和高度翻译相关性的结果的非侵入性、基于模型的预测因子，(3)在各种数学框架(如3D和隔室动力学系统)上开发稳健的参数识别和估计方法，(4)并使用这些模型将这些实验数据映射到现有的人类数据，并生成复杂疾病的非常早期的生物特征的预测。为了实现这些目标，这项提议将汇集一个数据库，其中包括有史以来收集到的最详细的多尺度、纵向观测，毫无疑问，除了提议的努力之外，其他生物和物理科学家小组也将使用这些数据库。这一提议的基本前提是，对IAV相关动物模型中的复杂生物学数据进行基于模型的解释，结合不完整但相关的人类数据，将导致复杂疾病的新预测生物标记物和新的治疗方法。

DESCRIPTION (provided by applicant): This proposal will join for the first time advanced computational algorithms, multi-scale mathematical models, state-of-the-art imaging and biological measurements, a highly relevant animal model, and existing human data towards a translational problem of imminent relevance to human health: pandemic influenza A virus infection (IAV) and its potential to cause critical illness and death in a large number of individuals. Indeed, the 2009 H1N1 influenza reassortant virus caused an estimated 18,000 deaths so far, targeted younger individuals and pregnant women, caused acute lung injury in close to half of cases admitted to the intensive care unit, and was also associated with a secondary bacterial infection in up to 35% of the these cases. Not only is 2009 H1N1 of major significance in itself and expected to display at least a third wave in the fall of 2010, but it represents a prototypical emerging infectious disease of pandemic proportion. As such, it offers an exceptional opportunity to deepen our understanding of (1) mechanisms associated with influenza a virus pathogenicity, (2) prognostic biomarkers of severity and of complicating bacterial infections, and (3) of the potential contribution of tools leveraged from the physical sciences to enhance knowledge and preparedness for the next "big one". We propose to (1) model real-time observations of cellular, inter-cellular processes and organ function at multiple levels including in vivo imaging, (2) develop non-invasive, model-based predictors of severe complications and of outcome of high translational relevance, (3) develop robust methods for parameter identification and estimation on a variety of mathematical frameworks such as 3D and compartmental dynamical systems, (4) and use these models to map these experimental data to existing human data and generate predictions of very early biosignatures of complicated disease. To achieve these goals, this proposal will assemble a database of the most detailed multiscale, longitudinal observations ever collected that will be undoubtedly used by other groups of biological and physical scientists beyond the proposed effort. The underlying premise of this proposal is that model-based interpretation of complex biological data in a relevant animal model of IAV, combined with incomplete, yet relevant human data, will lead to new predictive biomarkers of complicated illness and new therapeutic approaches.