Consortium for Viral Systems Biology Modeling Core

病毒系统生物学建模核心联盟

• 批准号: 10310604
• 负责人: Marc A. Suchard
• 金额: $ 5.77万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10310604
• 关键词: Affect; Algorithmic Software; Algorithms; Bioinformatics; Biological; Biology; Cataloging; Clinic; Clinical; Clinical Data; Communicable Diseases; Computer software; Consultations; Data; Development; Ebola virus; Evaluation; Event; Evolution; Experimental Designs; Functional disorder; Genetic; Genetic Determinism; Genetic Models; Genomics; Health; Human Resources; Immune response; Immunologics; Lassa virus; Marriage; Measurable; Measurement; Measures; Medicine; Modeling; Monitor; National Institute of Allergy and Infectious Disease; Outcome; Pathway Analysis; Patient Monitoring; Peer Review; Phylogenetic Analysis; Physiological; Population; Public Health; Publishing; Recording of previous events; Reproducibility; Research; Scientist; Statistical Algorithm; Survival Analysis; System; Systems Biology; Technology; Testing; Thinking; Time; Training; Validation; Variant; Viral; Viral Hemorrhagic Fevers; Virus Diseases; Wireless Technology; adaptive immunity; analysis pipeline; bioinformatics resource; biomathematics; burden of illness; combat; design; genetic association; genetic predictors; genomic data; mathematical algorithm; mathematical model; model building; multidisciplinary; next generation; open source; pathogen; reconstruction; response; sensor; statistics; survival prediction; theories; tool; user friendly software; viral genomics; virology; virtual; Affect; Algorithmic Software; Algorithms; Bioinformatics; Biological; Biology; Cataloging; Clinic; Clinical; Clinical Data; Communicable Diseases; Computer software; Consultations; Data; Development; Ebola virus; Evaluation; Event; Evolution; Experimental Designs; Functional disorder; Genetic; Genetic Determinism; Genetic Models; Genomics; Health; Human Resources; Immune response; Immunologics; Lassa virus; Marriage; Measurable; Measurement; Measures; Medicine; Modeling; Monitor; National Institute of Allergy and Infectious Disease; Outcome; Pathway Analysis; Patient Monitoring; Peer Review; Phylogenetic Analysis; Physiological; Population; Public Health; Publishing; Recording of previous events; Reproducibility; Research; Scientist; Statistical Algorithm; Survival Analysis; System; Systems Biology; Technology; Testing; Thinking; Time; Training; Validation; Variant; Viral; Viral Hemorrhagic Fevers; Virus Diseases; Wireless Technology; adaptive immunity; analysis pipeline; bioinformatics resource; biomathematics; burden of illness; combat; design; genetic association; genetic predictors; genomic data; mathematical algorithm; mathematical model; model building; multidisciplinary; next generation; open source; pathogen; reconstruction; response; sensor; statistics; survival prediction; theories; tool; user friendly software; viral genomics; virology; virtual

Project Summary/Abstract The Modeling Core targets the development, validation and refinement of models to predict pathogen genetic and host immune response and physiological features affecting viral hemorrhagic fever survival and long-term sequelae of Lassa virus (LASV) and Ebola virus (EBOV) infection. Our multidisciplinary team carries expertise across statistical thinking, mathematical modeling, evolutionary biology and computing to leverage sequenc- ing, immunological profiling, mobile sensor and clinical data. We provide to the Consortium for Viral Systems Biology Cores and Projects guidance in phylogenetic reconstruction to define evolutionary trajectories and cataloguing LASV and EBOV intra-host variants, genetic association studies mapping host determinants and, importantly, consultation on all statistical aspects of experimental design in the Projects. Our chief innova- tions are three-fold. First, we incorporate viral sequence evolution into predictive survival models through the development of phylogenetic survival analysis to uncover the viral and host genetic determinants of host time-to-event health outcomes while appropriately controlling for shared evolutionary history and incorporat- ing adaptive immunity repertoire development. We integrate large-scale non-omics data into these survival models using advancing computing technology to include time-dependent immunological and physiological features arising from wireless patient monitors and clinical tests. Third, we exploit systems-level prediction evaluation and refinement for iterative model building with internal validation, biological experimentation and network analysis. The Core will deliver effective analysis tools enabled for real-time and scriptable use in open- source, reproducible research and will marshall both hands-on short-courses and a regular virtual quantitative clinic to catalyze the interactions between modeling and experimentation.

项目摘要/摘要 建模核心针对模型的开发，验证和培养以预测病原体通用 宿主免疫响应和影响病毒出血热生存和长期的身体特征 LASSA病毒（LASV）和埃博拉病毒（EBOV）感染的后遗症。我们的多学科团队具有专业知识 在统计思维，数学建模，进化生物学和计算中，以利用序列 - ING，免疫学专业，移动传感器和临床数据。我们为病毒系统的财团提供 生物学核心和项目在系统发育重建方面的指导，以定义进化轨迹和 分类LASV和EBOV主宿内变体，遗传关联研究映射宿主的确定词，以及， 重要的是，就项目中实验设计的所有统计方面进行咨询。我们的首席Innova- tions是三倍。首先，我们通过 系统发育生存分析的发展，以发现宿主的病毒和宿主遗传决定剂 在适当控制共同进化史的同时，发生事件的健康结果，并纳入 自适应免疫剧集的发展。我们将大规模的非语言数据集成到这些生存中 使用前进的计算技术的模型包括时间依赖于时期的免疫和生理 由无线患者监视和临床测试引起的功能。第三，我们利用系统级预测 具有内部验证，生物学实验和 网络分析。核心将提供有效的分析工具，以实时和脚本使用，以便在开放中使用 来源，可重现的研究，并将在“动手短”和定期的虚拟定量 诊所以催化建模与实验之间的相互作用。