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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.

总结