Core E - Data Analysis and Modeling Core

Core E - 数据分析和建模核心

• 批准号: 10153661
• 负责人: JUN ZHU
• 金额: $ 30万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10153661
• 关键词: Address; Affect; Antibodies; Behavior; Biological; Biological Markers; Biological Models; Cell surface; Cells; Clinical; Communicable Diseases; Complex; Computer Analysis; Computer Models; Data; Data Analyses; Data Set; Dengue; Dengue Infection; Dengue Virus; Dimensions; Disease Outcome; Genes; Genetic Transcription; Genomics; Goals; High Performance Computing; Human; Immune; Immune response; Immune system; Immunization; Immunologics; Immunology; Individual; Infection; Investigation; Mathematics; Metabolism; Modeling; Molecular; Multiomic Data; Multivariate Analysis; Natural Immunity; Nature; Network-based; Outcome; Pathway Analysis; Pathway interactions; Patient risk; Phenotype; Predisposition; Process; Property; Proteomics; Quality Control; RNA; Risk; Series; Serology; 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; biomarker performance; cell mediated immune response; cell type; cytokine; data integration; data modeling; genetic variant; high dimensionality; insight; mathematical model; multiple omics; network models; novel; point of care testing; predict clinical outcome; programs; response; synergism; 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.

概括 对登革热病毒（DENV）宿主关系的系统级别的理解，尤其是网络结构和动力学，可以从实验数据中得出，并在数据集和建模之间进行计算分析。宿主对感染的反应是一个复杂的过程，涉及整个RNA转录，蛋白质信号传导和代谢的整个网络，该网络会互补地影响细胞，组织和整个生物体行为。这种复杂性需要一种系统生物学方法来理解免疫响应，因为对单个途径的研究不太可能解释整个网络中发生的变化。数据分析和建模核心（核心E）不仅将对每个数据集执行标准的多元分析，以找到可靠的生物标志物来区分感染结果，而且还将将它们与整个公共网络和途径数据集成在一起，以构建一个多尺度的生物学上有意义的DENV-host Itspetractions。由于该模型是定量和数学定义的，因此它非常适合训练高级分类器，这些分类器可以比单独的生物标志物和新颖的“关键驱动器”生物分子预测两个个性化的临床结果，并且可以通过Vivo Sierna Screens验证（项目3）。这些数据应进一步告知多个相互关联的分子途径和网络之间的协同作用，这些途径和网络是个人之间表型差异的基础。 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 super unbiased, data drive models that address the key biologic questions in each of the Projects emerge, explaining the divers subtleties of host response to DENV infection and vaccination that affect clinical outcomes.