Viruis Dynamics and Multiple Infection of Cells: Computational and Experimental A

病毒动力学和细胞多重感染：计算和实验 A

• 批准号: 8188288
• 负责人: DAVID N LEVY
• 金额: $ 38.9万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-03 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8188288
• 关键词: 3-Dimensional; Affect; Antiviral Agents; Biological; Biology; CD4 Positive T Lymphocytes; Cell Death; Cell Proliferation; Cells; Cessation of life; Clear Cell; Collaborations; Complement; Complex; Computer Simulation; Data; Development; Disease; Drug Formulations; Equation; Evolution; Foundations; Future; Genetic Recombination; Growth; HIV; HIV-1; Immune response; In Vitro; Individual; Infection; Kinetics; Laws; Lead; Lymphoid; Modeling; Outcome; Pathogenesis; Pharmaceutical Preparations; Play; Population; Preparation; Process; Production; Property; Proviruses; Rest; Role; Science; Structure; System; Target Populations; Testing; Therapeutic; Time; To specify; Translating; Validation; Variant; Viral; Viral Pathogenesis; Virus; Virus Diseases; Virus Replication; Work; base; cell type; clinically relevant; data modeling; design; in vivo; insight; macrophage; mathematical model; monolayer; novel; particle; reproductive; research study; response; transmission process; virology

DESCRIPTION (provided by applicant): The dynamics between virus populations and target cells in vitro and in vivo have been investigated extensively in the context of a variety of infections, both experimentally and with mathematical models. While this work has lead to many important insights into disease mechanisms and the efficacies of antiviral therapeutics, most of it has been based upon the assumption that individual cells are only infected with a single virus. In recent years, however, it has become clear that cells are frequently infected with multiple copies of the same virus in a variety of different infections. Such coinfection is likely to have a profound influence on viral dynamics and to influence the establishment of infection, viral spread, the course of disease and the response to antiviral drugs. The best experimental system to study coinfection is HIV, which is the focus of this proposal. We seek to provide a thorough and quantitative understanding of how virus replication kinetics and direct pathogenesis are influenced by coinfection, information which so far has been lacking. This will be done with HIV-1 in the context of 3 different target cell types in order to capture variation in viral replication and coinfection parameters. We subsequently aim to define how these replication kinetics translate into the dynamics of virus growth, as the virus spreads through its target cell population. This can only be achieved with the construction of mathematical models which capture the experimental data and make robust predictions regarding the dynamics of viral replication under different replication scenarios. Two fundamentally different modeling approaches will be considered, an ordinary differential equation model and an agent based model, and the relationship between them will be defined. This allows cross-validation between models and to overcome inherent weaknesses of individual modeling approaches. The model outcomes further define the experiments to be performed in order to test model predictions, which is a central component of our proposal. In addition to in vitro experiments, our analysis will be repeated using ex vivo lymphoid histoculture for comparison with cell monolayer monocultures, to provide higher clinical relevance of our studies. PUBLIC HEALTH RELEVANCE: Understanding the dynamics of viral replication is critically important to our understanding of viral pathogenesis, immune responses and for the development of novel therapies. Mathematical and in silico models of viral dynamics are central to developing this understanding. When combined with experimentation mathematical analyses generate significant, and importantly, testable concepts and hypotheses concerning how viruses establish infection, replicate, persist, cause disease, and most importantly, how we may control or eliminate them.

描述（由申请人提供）：在实验和数学模型的各种感染的背景下，已经对病毒群体和靶细胞之间的动力学进行了广泛研究。尽管这项工作已导致许多重要的见解对疾病机制和抗病毒疗法的疗效，但大多数是基于以下假设：单个细胞仅感染了单一病毒。然而，近年来，很明显，在各种不同的感染中，细胞经常被同一病毒的多个副本感染。这种共感染可能对病毒动力学产生深远的影响，并影响感染，病毒传播，疾病进程和对抗病毒药的反应的建立。研究共感染的最佳实验系统是HIV，这是该提案的重点。我们试图对病毒复制动力学和直接发病机理的影响以及迄今为止缺乏的信息的影响提供彻底的定量理解。这将在3种不同的目标细胞类型的背景下用HIV-1完成，以捕获病毒复制和共感染参数的变化。随后，随着病毒通过其靶细胞群体扩散，我们的目标是定义这些复制动力学如何转化为病毒生长的动力学。这只能通过构建数学模型来捕获实验数据并就不同复制情景下病毒复制的动力学做出强大的预测来实现。将考虑两种根本不同的建模方法，即一个普通的微分方程模型和一个基于代理的模型，并将定义它们之间的关系。这允许模型之间的交叉验证并克服单个建模方法的固有弱点。该模型的结果进一步定义了要进行的实验，以测试模型预测，这是我们建议的核心组成部分。除了体外实验外，我们的分析还将使用离体淋巴组织培养物重复，以与细胞单层单一培养物进行比较，以提供我们研究的较高临床相关性。 公共卫生相关性：了解病毒复制的动态对于我们对病毒发病机理，免疫反应和新疗法的发展至关重要。数学和病毒动力学模型中的数学模型对于发展这种理解至关重要。当与实验分析结合使用时，关于病毒如何建立感染，复制，持久，引起疾病，最重要的是，我们如何控制或消除它们，就会产生重要的，重要的，可检验的概念和假设。