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: 8510568
• 负责人: DAVID N LEVY
• 金额: $ 35.19万
• 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/8510568
• 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.

描述(申请人提供)：病毒群体和目标细胞之间的动态在体外和体内已经在各种感染的背景下进行了广泛的研究，包括实验和数学模型。虽然这项工作对疾病机制和抗病毒疗法的有效性有了许多重要的见解，但大多数都是基于单个细胞只感染一种病毒的假设。然而，近年来，在各种不同的感染中，细胞经常感染同一病毒的多个副本，这一点已经变得很清楚。这种混合感染很可能对病毒动力学产生深远的影响，并影响感染的确立、病毒的传播、病程和对抗病毒药物的反应。研究混合感染的最佳实验系统是艾滋病毒，这是这项提案的重点。我们试图对病毒复制动力学和直接致病机制如何受到混合感染的影响提供彻底和定量的理解，这方面的信息到目前为止还很缺乏。这将在3种不同目标细胞类型的背景下对HIV-1进行，以捕捉病毒复制和联合感染参数的变化。我们随后的目标是定义当病毒通过其目标细胞群传播时，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实现，该数学模型捕捉实验数据并对不同复制场景下的病毒复制动态做出可靠的预测。将考虑两种根本不同的建模方法，常微分方程模型和基于代理的模型，并定义它们之间的关系。这允许模型之间的交叉验证，并克服了单个建模方法的固有弱点。模型结果进一步定义了为了测试模型预测而要执行的实验，这是我们提案的核心组成部分。除了体外实验，我们的分析将重复使用体外淋巴组织培养与细胞单层培养进行比较，以提供更高的临床相关性的研究。