COBRE: UID: PROJ 2: SPATIAL STRUCTURE AND ADAPTIVE EVOLUTION OF VIRUSES

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 自然和临床环境中的大多数微生物群落在空间结构上是有结构的，表现为表面相关的种群，如生物膜。这种结构深刻地影响着塑造社区的生态和进化力量。这在与不断进化的人类病原体的斗争中尤为重要，这些病原体的生态和进化时间尺度比人类时间尺度短得多。尽管如此，关于空间异质性对微生物病原体适应性进化的具体影响还知之甚少。因此，建议进行一项联合实验和理论研究，以揭示空间结构在驱动病毒进化和生态动力学中所起作用的基本细节。一个简单的实验模型系统将为解决这些问题提供一个框架。该实验系统将用于研究在变化的环境条件下感染琼脂平板上细菌宿主的噬菌体的分子适应性进化。与这些实验相结合，将开发新的空间显式数学模型(随机细胞自动机)，并将其适用于经验观察，目的是预测微生物系统的行为，揭示在病毒的适应性进化中最重要的潜在生物学机制，并产生新的假设。该项目将侧重于空间结构对环境单一变化的适应性进化的影响，并将为独立的R01赠款申请生成初步数据，该申请涉及在环境条件波动的情况下进行适应性进化。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Most microbial communities in natural and clinical settings are spatially structured, appearing as surface-associated populations such as biofilms. This structure profoundly affects the ecological and evolutionary forces that shape the community. This is particularly important in the battle against ever-evolving human pathogens that have ecological and evolutionary time scales much shorter than human time scales. Despite this, very little is known about the specific effects of spatial heterogeneity on the adaptive evolution of microbial pathogens. Thus, a joint experimental and theoretical investigation is proposed to uncover fundamental details about the role of spatial structure in driving evolutionary and ecological dynamics of viruses. A simple experimental model system will provide a framework for addressing these issues. This experimental system will be used to study the molecular adaptive evolution of bacteriophages infecting their bacterial hosts on agar plates under changing environmental conditions. In conjunction with these experiments, new spatially explicit mathematical models (stochastic cellular automata) will be developed and fit to empirical observations with the aims of predicting the behavior of the microbial systems, uncovering the underlying biological mechanisms that are most important in the adaptive evolution of viruses, and generating new hypotheses. This project will focus on the impact of spatial structure on adaptive evolution to a single change in environment and will generate preliminary data for an independent R01 grant application that addresses adaptive evolution in the presence of fluctuating environmental conditions.