Core E - Data Analysis and Modeling Core

Core E - 数据分析和建模核心

• 批准号: 8929550
• 负责人: ERIC E SCHADT
• 金额: $ 18.86万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8929550
• 关键词: Address; Affect; Antibodies; Bedside Testings; Behavior; Biological; Biological Markers; Cell surface; Cells; Clinical; Communicable Diseases; Complex; Computer Analysis; Computer Simulation; Data; Data Analyses; Data Set; Dengue; Dengue Virus; Dimensions; Disease Outcome; Genes; Genetic Transcription; Genomics; Goals; High Performance Computing; Human; Immune; Immune response; Immune system; Immunization; Immunology; Individual; Infection; Investigation; Maps; Metabolism; Modeling; Molecular; Multivariate Analysis; Natural Immunity; Nature; Network-based; Outcome; Pathway Analysis; Pathway interactions; Patient risk; Phenotype; Predisposition; Process; Property; Proteomics; Quality Control; RNA; Relative (related person); Risk; Series; Serological; Serotyping; Severity of illness; Signal Transduction; Signaling Protein; Small Interfering RNA; Structure; System; Systems Biology; Time; Tissues; Training; Vaccination; Vaccines; Variant; Virus Diseases; Whole Organism; adaptive immunity; base; cell mediated immune response; cell type; cytokine; data integration; data modeling; genetic variant; insight; mathematical model; network models; novel; programs; response; transcriptomics; virus genetics; virus host interaction

SUMMARY A system-level understanding of the dengue virus (DENV) host relationship, in particular, the network structure and dynamics, can be derived from experimental data with computational analysis across data sets and modeling. The host response to infection is a complex process involving entire networks of RNA transcription, protein signaling, and metabolism that complementarily influence cellular, tissue, and whole organism behaviors. This complexity demands a systems biology approach for understanding immune response, since investigation of single pathways is unlikely to explain changes taking place across the entire network. The Data Analysis and Modeling Core (Core E) will not only perform standard multivariate analyses on each dataset to find reliable biomarkers for differentiating outcomes of infection, but will furthermore integrate them with the full range of public network and pathway data to construct a multiscale, holistic network model of biologically meaningful DENV-host interactions. Because this model is quantitative and mathematically defined, it is well suited for training advanced classifiers that can predict both individualized clinical outcomes with more accuracy than biomarkers alone and novel “key driver” biomolecules that can be validated with ex vivo siRNA screens (Project 3). These data should further inform on the synergy among multiple interrelating molecular pathways and networks that underpin the differences in phenotype between individuals. The scale of our proposed model for DENV is unprecedented, spanning the genomic, transcriptomic, proteomic, intercellular signaling, and immune cell subpopulation levels—and only with this scale of modeling will superior unbiased, data driven models that address the key biological questions in each of the Projects emerge, explaining the diverse subtleties of host response to DENV infection and vaccination that affect clinical outcomes.

总结 系统级了解登革病毒（DENV）宿主关系，特别是网络结构和 动力学，可以从实验数据中得到，并通过数据集和建模进行计算分析。的 宿主对感染的反应是一个复杂的过程，涉及RNA转录、蛋白质信号传导和 补充影响细胞、组织和整个生物体行为的代谢。这种复杂性要求 一个系统生物学的方法来理解免疫反应，因为调查的单一途径是不可能的， 解释整个网络中发生的变化。数据分析和建模核心（Core E）不仅 对每个数据集进行标准的多变量分析，以找到可靠的生物标志物来区分 感染，但将进一步整合他们与全方位的公共网络和途径数据，以构建一个 生物学意义的DENV-宿主相互作用的多尺度整体网络模型。因为这个模型是 定量和数学定义，它非常适合训练高级分类器，可以预测 个性化的临床结果，比单独的生物标志物更准确，以及新型“关键驱动”生物分子， 可以用离体siRNA筛选来验证（项目3）。这些数据应进一步说明 多个相互关联的分子通路和网络，这些通路和网络支持了表型的差异， 个体我们提出的DENV模型的规模是前所未有的，跨越了基因组，转录组， 蛋白质组学，细胞间信号传导和免疫细胞亚群水平-只有这样的建模规模将 出现了解决每个项目中关键生物学问题的上级无偏见的数据驱动模型， 解释宿主对DENV感染和疫苗接种反应的不同微妙之处，这些反应影响临床结果。