Core C - Modeling Core

01.项目摘要 流感感染导致不同的临床结果，从良性症状到住院治疗， 有时死亡（在美国每年有12，000至56，000人死亡）。然而，生物标志物 预测流感疾病的结果（即症状严重程度，病毒复制），这是预防的关键 药物尚未确定。此外，阐明了控制预测的分子组分， 特征将提供指导新治疗策略发展的重要线索。U19 将建立在以前FluOMICS联盟已经收集的大量数据的基础上，并追求两个融合 假设1）多个和离散的宿主免疫应答途径协同作用以确定致病性 流感感染的结果2）流感和这些宿主途径之间的串扰导致 在多种组织中建立相关的表观遗传、转录、翻译后、代谢特征 和细胞类型。建模核心的主要目标是使用基于网络的建模方法， 整合项目1和项目2产生的实验数据，确定生物过程， 与项目1和项目2合作，预测流感疾病的结果并对其进行功能验证。 在目标1中，建模核心与技术核心和数据管理部门合作， 生物信息学核心，将预处理，应用适当的统计分析和解释所有大规模的 （OMIC）项目1和项目2中生成的数据集。我们将提供数据集成和可视化表示 对于每个OMIC数据集和总结表，给出差异表达标记的数量和身份 （基因/蛋白质/翻译后修饰/代谢物）。在目标2中， Modeling Core将通过将所有这些数据集映射到转录网络来提供所有这些数据集的综合视图。 节点和信号转导途径，它们由不同的感染条件不同地触发， 模仿宿主之间的差异。关键是，建模核心将应用启发式方法和迭代方法。 项目1和项目2的过程，以解开和改善生物网络之间的相关性， 正交样本类型（即人/小鼠血液、小鼠肺和人细胞）中收集的途径 由核心产生。在目标3中，建模核心将使用机器学习技术来生成预测 基于一组优先标记的模型，能够预测对与各种疾病相关的流感病毒株的反应 states.这些标志物将包括预测临床结果的宿主因素和宿主-病原体相互作用， 可能与症状严重程度有关。建模核心将站在这个U19作为最终的 基础设施和资源，将整合在此计划中生成的大量数据，并提供给 科学界重要的可交付成果，即疾病严重程度的有效特征， 预防医学和临床结果多样性的机械线索。

01. PROJECT SUMMARY Influenza infection leads to different clinical outcomes that range from benign symptoms to hospitalization and sometimes death (ranging from 12,000 to 56,000 deaths per year in the United States). However, biomarkers predictive of influenza disease outcomes (i.e. symptoms severity, viral replication) which are key for preventive medicine have not yet been identified. Moreover, elucidation of the molecular components that govern predictive signatures will provide important clues that will guide the development of novel therapeutic strategies. This U19 will build on a wealth of data gathered already by the previous FluOMICS consortium and pursue two converging hypotheses 1) multiple and discrete host immune response pathways act in concert to determine the pathogenic outcome of influenza infection 2) the crosstalk between influenza and these host pathways results in the establishment of correlated epigenetic, transcriptional, post-translational, metabolic signatures in multiple tissues and cell types. The major objective of the Modeling Core will be to use network-based modeling approaches to integrate the experimental data generated by Project 1 and Project 2, identify biological processes that can predict influenza disease outcomes and validate them functionally in collaboration with Project 1 and Project 2. In Aim 1 the Modeling Core, in collaboration with the Technology Core and the Data Management and Bioinformatics Core, will preprocess, apply the appropriate statistical analysis and interpret all large-scale (OMIC) datasets generated in Project 1 and Project 2. We will provide data integration and visual representation for each OMIC dataset and summary tables giving the number and identity of differentially expressed markers (genes/proteins/post-translational modifications/metabolites) associated with disease outcomes. In Aim 2, the Modeling Core will provide an integrated view of all these datasets by mapping them to networks of transcriptional nodes and signal transduction pathways which are differentially triggered by different conditions of infection and mimic differences between hosts. Critically, the Modeling Core will apply heuristic approaches and an iterative process with the Project 1 and Project 2 to unravel and improve on correlations between networks of biological pathways collected in orthogonal sample types (i.e. human/mouse blood, mouse lungs, and human cells) generated by the cores. In Aim 3 the Modeling Core will use a machine learning technique to generate predictive models based on prioritized set of markers able to predict responses to influenza strains linked to various disease states. These markers will include host factors and host-pathogen interactions that predict clinical outcomes and may be mechanistically implicated in symptoms severity. The Modeling Core will stand in this U19 as the ultimate infrastructure and resource that will integrate the large body of data generated in this program and provide to the scientific community important deliverables namely validated signatures of disease severity, biomarkers for preventive medicine and mechanistic cues to the diversity of clinical outcomes.!