Core E - Data Analysis and Modeling Core

Core E - 数据分析和建模核心

基本信息

批准号：
9293238
负责人：
ERIC E SCHADT
金额：
$ 51.32万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
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/9293238
关键词：
Address Affect Antibodies Bedside Testings Behavior Biological Biological Markers Biological Models Cell surface Cells Clinical Communicable Diseases Complex Computer Analysis Computer Simulation 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 Multivariate Analysis Natural Immunity Nature Network-based Outcome Pathway Analysis Pathway interactions Patient risk Phenotype Predisposition Process Property Proteomics Quality Control RNA 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 high dimensionality insight mathematical model network models novel predict clinical outcome programs response synergism transcriptomics virus genetics virus host interaction

项目摘要

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.

概括系统级对登革热病毒（DENV）宿主关系的理解，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，蛋白质信号传导和互补影响细胞，组织和整个生物体行为的代谢。这种复杂性需要一种用于理解免疫反应的系统生物学方法，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）不仅将在每个数据集上执行标准的多元分析，以找到可靠的生物标志物来区分结果感染，但此外将将它们与全部公共网络和途径数据集成在一起，以构建一个生物学上有意义的DENV-host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，它非常适合培训可以预测的高级分类器单独使用生物标志物和新颖的“主要驱动力”生物分子，具有更准确的个性化临床结果可以用离体siRNA屏幕验证（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，这些途径是基于表型差异的基础个人。我们提出的DENV模型的规模是前所未有的，涵盖了基因组，转录组，蛋白质组学，细胞间信号传导和免疫细胞亚群水平 - 仅在这种模型范围内卓越无偏的数据驱动模型，这些模型解决了每个项目中每个项目中关键的生物学问题，解释了影响临床结果的宿主对DENV感染和疫苗接种的宿主反应的微妙之处。