QuBBD: Viral Evolution and Spread of Infectious Disease in Complex Network: Big Data Analysis and Modeling

概述：高度可变的RNA病毒，如人类免疫缺陷病毒和丙型肝炎病毒 世界上发病率和死亡率的主要原因。RNA病毒的特点是其极高的 基因多样性，使它们能够迅速建立新的感染，逃避宿主的免疫系统，并 产生抗药性。下一代测序技术的出现有望带来革命性的变化 病毒学和流行病学领域通过允许对数百万宿主内病毒进行采样和表征 数以千计的感染者体内存在变异。然而，我们对疾病传播机制的理解 和病毒进化仍然有限，因为缺乏处理、整合和 生物医药大数据分析。该项目的总体目标是开发一个全面的 允许描述、分析、理解和预测复杂情况的创新算法和模型 多维非线性疾病动力学。 智力优势：拟议的研究将由一个由以下人员组成的跨学科团队进行 生物学家、数学家、分子流行病学家和计算机科学家 与项目相关的领域。该项目将针对非常重要的流行病学和生物医学。 问题包括为疾病监测开发高效和可扩展的计算方法 通过结合宿主内和宿主间进化建立流行病学动力学的传播模型 将动力学整合到单一框架中，并设计计算工具以利用数据分析结果 医疗保健专业人士。所提出的算法和模型将通过使用大分子和 由CDC和佐治亚理工学院的项目合作者生成的流行病学数据，以及可用的 来自公共来源。这些算法将免费分发给研究人员和医疗工作者 开源包和基于云的在线工具。特别是，它们将被合并到全球 目前正在开发的基于Web的数据分析系统--卫生暴发和监测技术 在疾控中心。研究成果将通过期刊出版物和会议广泛传播 演讲，包括国际生物信息学研究与应用研讨会和 由PIS组织的分子流行病学计算进展研讨会。

Overview: Highly mutable RNA viruses, such as human immunodeficiency virus and hepatitis C virus are major causes of morbidity and mortality in the world. The hallmark of RNA viruses is their extremely high genetic diversity that allows them to rapidly establish new infections, escape host's immune system and develop drug resistance. Emergence of next-generation sequencing technologies promises to revolutionize the fields of virology and epidemiology by allowing to sample and characterize millions of intra-host viral variants in thousands of infected individuals. However, our understanding of mechanisms of disease spread and viral evolution are still limited due to the lack of computational methods for processing, integration and analysis of biomedical big data. The overarching goal of this project is to develop a comprehensive family of innovative algorithms and models that allow to describe, analyze, understand and predict complex multidimensional non-linear disease dynamics. Intellectual Merit: The proposed research will be conducted by an interdisciplinary team comprised of biologists, mathematicians, molecular epidemiologists and computer scientists with extensive expertise in the areas relevant to the project. The project will target highly important epidemiological and biomedical problems including development of efficient and scalable computational methods for surveillance of disease spread, modeling of epidemiological dynamics by incorporation of intra-host and inter-host evolutionary dynamics into a single framework and design of computational tools for utilization of data analysis results by health care professionals. Proposed algorithms and models will be validated using massive molecular and epidemiological data generated by project collaborators from CDC and Georgia Tech, as well as available from public sources. The algorithms will be distributed to the researchers and health care workers as free open-source packages and cloud-based online tools. In particular, they will be incorporated in the Global Health Outbreak and Surveillance Technology, a web-based data analysis system currently being developed at CDC. Research findings will be broadly disseminated via journal publications and conference presentations, including the International Symposium on Bioinformatics Research and Applications and Workshop on Computational Advances in Molecular Epidemiology organized by the Pis.