Mathematical and computer models of viral infection dynamics within a host or a cell culture

宿主或细胞培养物内病毒感染动态的数学和计算机模型

• 批准号: 355837-2013
• 负责人: Beauchemin, Catherine
• 金额: $ 1.31万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=527918
• 关键词: Mathematical; computer; models; viral; infection

Experimentation in vitro and in vivo has traditionally been the only way to study viral infections. This approach for deriving knowledge relies on common-sense assumptions (e.g., a higher viral count means a fitter virus). These assumptions often go untested due to difficulties controlling individual components of these complex systems without affecting others. Mathematical and computer models (MCMs), however, make it possible to deconstruct an experimental system into individual components and determine how the pieces combine to create the infection we observe. Virophysics is an important branch of biophysics in which the theories and methods of physics are applied to study the mechanics and dynamics of viruses. The long-term objective of my research programme is to develop and improve MCMs to accurately model viral infection spread within a cell culture or a host. Expressing the process of viral infection in the form of MCMs is essential because it enables us to predict and, ultimately, control the course and outcome of an infection. The specific aims of this proposal are to develop new MCMs for: (1) the effect of defective interfering flu virus particles; (2) the spatial distribution of a flu infection and transport of the virus within the human respiratory tract; and (3) the spread of hepatitis C virus infections in liver cell cultures. Experimental data required to construct and validate the MCMs are collected by my collaborators working with influenza (G. Boivin) and hepatitis C (J. Feld). They, in turn, use my MCMs' predictions to evaluate, for example, the likelihood of emergence of a drug-resistant virus strain or what aspect of a virus' replication cycle is the optimal target for antiviral drugs. With the increasing sophistication of experimental methods in virology, the vast amount of quantitative data generated precludes the simple statistical analyses of the past: it is now critical for virologists to collaborate with researchers from more traditionally quantitative fields such as Physics. Physicists are ideally suited to make important contributions in this area, and my group is one of the few conducting this type of research in Canada.

传统上，体外和体内实验是研究病毒感染的唯一方法。这种获取知识的方法依赖于常识性假设（例如，较高的病毒计数意味着更适合的病毒）。这些假设往往未经检验，因为很难控制这些复杂系统的单个组件而不影响其他组件。然而，数学和计算机模型（MCM）使我们有可能将实验系统解构为单个组件，并确定这些组件如何结合联合收割机来创建我们观察到的感染。病毒物理学是生物物理学的一个重要分支，它应用物理学的理论和方法研究病毒的力学和动力学。我的研究计划的长期目标是开发和改进MCMs，以准确模拟细胞培养物或宿主内的病毒感染传播。以MCMs的形式表达病毒感染的过程是至关重要的，因为它使我们能够预测并最终控制感染的过程和结果。该提案的具体目标是开发新的MCM，用于：（1）有缺陷的干扰流感病毒颗粒的影响;（2）流感感染的空间分布和病毒在人体呼吸道内的运输;以及（3）丙型肝炎病毒感染在肝细胞培养物中的传播。构建和验证MCMs所需的实验数据是由我的合作者收集的，他们与流感（G。Boivin）和丙型肝炎（J.费尔德）。反过来，他们使用我的MCMs的预测来评估，例如，出现耐药病毒株的可能性，或者病毒复制周期的哪个方面是抗病毒药物的最佳靶点。随着病毒学实验方法的日益复杂，产生的大量定量数据排除了过去简单的统计分析：现在病毒学家与物理学等传统定量领域的研究人员合作至关重要。物理学家非常适合在这一领域做出重要贡献，我的团队是加拿大为数不多的进行此类研究的团队之一。