Center for Viral Systems Biology (CViSB)

病毒系统生物学中心（Center for Viral Systems Biology，CViSB） 2019冠状病毒病大流行鲜明地提醒人们注意传染病构成的威胁，但其他优先事项 拉沙病毒和埃博拉病毒等病原体继续在流行地区构成重大挑战。 感染拉沙、埃博拉和SARS-CoV-2病毒可导致不同的急性和长期结果， 范围从轻微或无症状疾病到长期后遗症或死亡。当我们建立了 在2018年的病毒系统生物学中心（CViSB，cvisb.org），我们设定了一个主要目标，即回答为什么。与 我们专注于埃博拉、拉沙，并通过扩大范围、COVID-19，通过确定 决定疾病结果的临床、免疫学、遗传和病毒分子因素。 在这个更新的应用程序中，我们将超越标准的系统生物学数据生成和分析 并使用反向翻译的方法来扩展我们以前的工作，重点放在两个基本的 研究和翻译应用。我们的核心假设仍然是病毒的复杂网络 以及人为因素，包括不同的临床、免疫学、遗传学、病毒学和生理学因素 属性在决定拉沙、埃博拉和COVID-19的结果和传播方面发挥着关键作用。我们 总体目标是识别这些分子网络，并提供对这些分子网络的深入系统级理解。 病毒，宿主和疾病严重程度和传播的环境驱动因素，以发现疾病的预测标志物。 人类疾病我们将通过应用“组学”技术、可穿戴设备和 西部地区独特患者、疫苗接种者和幸存者队列高通量实验方法 非洲和美国。围绕两个相互关联的项目和四个核心，我们将完成 主要目标如下：（1）将复杂系统免疫学，宿主遗传学，生理学， 临床和代谢数据集，以执行预测建模并定义描述 使用西非和美国的队列研究拉沙、埃博拉和COVID-19的风险、严重程度和结果， States. (2)我们将把大规模的宿主病毒基因组数据集与人口数据相结合， 建立多元网络分析和动态增长的高通量实验方法 模型来阐明分子流行病学，功能进化，适应性免疫， 拉沙病毒、埃博拉病毒和SARS-CoV-2的宿主-病原体动态。(3)我们将扩大和发展 高通量实验方法、开源软件、机器学习工具和基于Web的 分析和可视化大规模数据集的平台，可实现实时询问， 解释复杂的宿主-病原体-环境网络结构和动态。(4)我们将 继续开发和维护强大的数据库和应用程序编程接口， 开源CViSB生成系统生物学数据集，并进行推广和培训，以促进 利用系统生物学研究传染病。

Project Summary - Center for Viral Systems Biology (CViSB) The COVID-19 pandemic is a stark reminder of the threat posed by infectious diseases, but other priority pathogens, such as Lassa and Ebola viruses, continue to pose significant challenges in endemic areas. Infection with Lassa, Ebola, and SARS-CoV-2 viruses can lead to diverse acute and long-term outcomes, ranging from mild or asymptomatic disease to long-term sequelae or death. When we established the Center for Viral Systems Biology (CViSB, cvisb.org) in 2018, we set a main goal of answering why. With a focus on Ebola, Lassa, and via a scope expansion, COVID-19, we have achieved this goal by identifying clinical, immunological, genetic, and virus molecular factors that determine the outcome of disease. In this renewal application, we will move beyond standard systems biology data generation and analysis and use a reverse translational approach to expand on our prior work with a focus on both fundamental research and translational application. Our central hypothesis remains that complex networks of viral and human factors, including distinct clinical, immunological, genetic, virological, and physiological attributes play key roles in determining the outcome and spread of Lassa, Ebola, and COVID-19. Our overall goal is to identify these molecular networks and provide a deep system-level understanding of the virus, host, and environmental drivers of disease severity and spread to discover predictive markers of human disease. We will successfully achieve this goal by applying ‘omics’ technologies, wearables, and high-throughput experimental approaches to unique patient, vaccinee, and survivor cohorts in West Africa and the United States. Built around two interconnected projects and four cores, we will complete the following major goals: (1) we will integrate complex systems immunology, host genetic, physiological, clinical, and metabolic datasets to perform predictive modeling and define biosignatures that describe the risk, severity, and outcome of Lassa, Ebola, and COVID-19 using cohorts in West Africa and the United States. (2) We will integrate large-scale host-virus genomic datasets with population data and high-throughput experimental approaches to build multivariate network analyses and dynamic growth models to elucidate the molecular epidemiology, functional evolution, adaptive immunity, and host-pathogen dynamics of Lassa virus, Ebola virus, and SARS-CoV-2. (3) We will expand and develop high-throughput experimental methods, open-source software, machine learning tools, and web-based platforms to analyze and visualize large-scale datasets that enable real-time interrogation and interpretation of complex host-pathogen-environment network structures and dynamics. (4) We will continue to develop and maintain robust databases and application programming interfaces for open-source CViSB generated systems biology datasets and perform outreach and training to promote the use of systems biology to study infectious diseases.