Consortium for Viral Systems Biology Modeling Core

病毒系统生物学建模核心联盟

• 批准号: 10579085
• 负责人: Marc A. Suchard
• 金额: $ 7.5万
• 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/10579085
• 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; 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; adaptive immunity; analysis pipeline; bioinformatics resource; biomathematics; burden of illness; combat; design; genetic association; genetic predictors; genomic data; long-term sequelae; mathematical algorithm; mathematical model; mobile sensor; model building; multidisciplinary; next generation; open source; pathogen; reconstruction; response; statistics; survival prediction; theories; tool; user friendly software; viral genomics; virology; virtual; wireless; wireless sensor

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 建模的核心目标是开发、验证和修复预测病原体基因的模型 而宿主的免疫反应和生理特征影响着病毒性出血热的生存和长期 拉萨病毒(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，以利用序列- ING、免疫学指标、移动传感器和临床资料。我们向病毒系统联盟提供 在系统发育重建中的生物学核心和项目指导到define进化轨迹和 编目LASV和EBOV宿主内变异，遗传关联研究绘制宿主决定因素， 重要的是，就项目中实验设计的所有统计方面进行咨询。我们的首席创新者- 申请有三个方面。首先，我们将病毒序列进化结合到预测生存模型中 系统发育生存分析在揭示宿主病毒和宿主遗传决定因素中的进展 事件发生前的健康结果，同时适当控制共享的进化历史并纳入- ING获得性免疫谱系研制。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依赖时间的免疫学和生理学模型 无线患者监护仪和临床测试所产生的特性。第三，我们利用系统级预测 通过内部验证、生物实验和其他方法对迭代模型建立的评估和改进 网络分析。核心将提供有效的分析工具，使其能够在开放的 来源，可重复的研究，并将编排实际操作的短期课程和常规的虚拟定量 临床催化建模和实验之间的互动。