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

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.

项目概要/摘要 建模核心的目标是开发、验证和完善模型以预测病原体遗传 影响病毒性出血热存活和长期的宿主免疫反应和生理特征 拉沙病毒（LASV）和埃博拉病毒（EBOV）感染的后遗症。我们的多学科团队拥有专业知识 跨越统计思维、数学建模、进化生物学和计算来利用序列 分析、免疫分析、移动传感器和临床数据。我们向病毒系统联盟提供 系统发育重建的生物学核心和项目指导，以定义进化轨迹和 对 LASV 和 EBOV 宿主内变异进行编目，绘制宿主决定因素的遗传关联研究， 重要的是，就项目中实验设计的所有统计方面进行咨询。我们的主要创新- 系统蒸发散有三重。首先，我们通过以下方式将病毒序列进化纳入预测生存模型： 系统发育生存分析的发展以揭示宿主的病毒和宿主遗传决定因素 事件发生时间的健康结果，同时适当控制共同的进化历史并纳入 荷兰国际集团适应性免疫库的发展。我们将大规模的非组学数据整合到这些生存中 使用先进计算技术的模型包括时间依赖性免疫学和生理学 无线患者监护仪和临床测试产生的功能。第三，我们利用系统级预测 通过内部验证、生物实验和迭代模型构建的评估和细化 网络分析。该核心将提供有效的分析工具，可在开放式环境中进行实时和可脚本化的使用。 来源、可重复的研究，并将安排实践短期课程和定期虚拟定量课程 临床催化建模和实验之间的相互作用。