Understanding the Effects of Complex Phage-Bacteria Infection Networks on Marine Ecosystems

细菌和它们的病毒（细菌）构成海洋中数量最多、遗传多样性最大的两类生物。随着环境排序的出现，这种多样性的程度变得越来越明显。然而，到目前为止，新的分类学多样性的不断发现已经超过了量化细菌和细菌之间的功能和相互作用的进展。提高对不同群体的微生物如何利用细菌宿主的定量理解，将改善微生物种群动态，生态系统功能和全球生物地球化学循环的大规模动态的预测。该项目将建立一个理论框架，以确定复杂的噬菌体-细菌相互作用对海洋生态系统结构和功能的影响。该理论框架是基于噬菌体与其细菌宿主的交叉感染试验的分析，称为噬菌体-细菌感染网络（PBIN）。最近发现的结构的PBIN将结合一种新的生态进化动力学建模框架，在服务于以下目标：目标1。发展理论方法来分析PBIN，包括定量感染数据，以表征在海洋生态系统中发现的交叉感染的复杂模式。建立生态进化的多菌株模型，结合复杂的PBIN数据，以评估关于PBIN内交叉感染如何影响社区稳定性的假设。利用多菌株模型预测PBIN如何影响：（i）病毒与细菌种群丰度的比例;（ii）生态系统水平上的碳和营养物质通量。本项目发展的理论将改进海洋生态系统中噬菌体-细菌相互作用的表征，并建立将噬菌体-细菌相互作用与生态系统功能联系起来的框架。首先，该项目将通过开发可应用于定量感染数据的新型网络理论，概括经验PBIN中多尺度结构的初步发现。海洋PBIN的属性将进行分析，以评估它们是否是分层组织，组织成模块，和/或拥有多尺度结构。PBIN的统计结构将与多尺度协同进化模型集成。这些共同进化模型将被用来评估假设，交叉感染结构如何影响社区的稳定性。最后，这些共同进化模型将被用来考虑通过细菌宿主的病毒裂解的碳和营养再生。PBIN的结构将是不同的，以建立交叉感染和生态系统结构和功能的关键指标之间的联系，具体应用于玫瑰球菌和聚球藻宿主。分析方法和大规模模拟将被用来实现这些目标，与经验数据集密切相关。更广泛的影响：教育目标将围绕培养对微生物系统感兴趣的下一代定量生物学家的主题（目标4）。在此过程中，PI将：（i）为定量生物学家提供培训计划，使他们能够与不同背景的学生直接互动;（ii）引入一门专注于定量病毒生态学的新课程;（iii）开发和传播软件工具，使生物学家能够将严格的定量方法应用于病毒-宿主相互作用数据和病毒-宿主群落的研究。两名研究生和八名本科生将直接支持这个项目。这些学生将拥有从物理学到生物学的学术背景，并在协作团队中工作。研究生将前往亚利桑那大学和田纳西-诺克斯维尔大学的病毒生态学实验室进行长期访问。一个新的研究生课程将开发定量病毒生态学，以服务于这项赠款的学员和越来越多的学生感兴趣的环境微生物学在格鲁吉亚技术。在这个项目中开发的理论将作为开放源码软件工具加以实施和传播。

Bacteria and their viruses (phages) make up two of the most abundant and genetically diverse groups of organisms in the oceans. The extent of this diversity has become increasingly apparent with the advent of environmental sequencing. However, the ongoing discovery of new taxonomic diversity has, thus far, out-paced gains in quantifying the function of and interactions among phages and bacteria. Improved quantitative understanding of how diverse groups of phages exploit bacterial hosts will improve predictions of microbial population dynamics, ecosystem functioning, and the large-scale dynamics of global biogeochemical cycles. This project will develop a theoretical framework for characterizing the effect of complex phage-bacteria interactions on marine ecosystem structure and function. The theoretical framework is grounded in the analysis of cross-infection assays of bacteriophages with their bacterial hosts, termed phage-bacteria infection networks (PBINs). Recent discoveries concerning the structure of PBINs will be combined with a novel eco-evolutionary dynamics modeling framework in the service of the following aims: Aim 1. Develop theoretical methods to analyze PBINs that include quantitative infection data to characterize complex patterns of cross-infection found in marine ecosystems.Aim 2. Establish eco-evolutionary multi-strain models that incorporate complex PBIN data to evaluate hypotheses regarding how cross-infection within PBINs affects community stability.Aim 3. Utilize the multi-strain model to predict how PBINs influence: (i) the ratio of viral to bacterial population abundances; and (ii) the flux of carbon and nutrients at the ecosystem level.The theory developed in this project will improve characterizations of phage- bacteria interactions in marine ecosystems and establish a framework to link phage-bacteria in- teractions with ecosystem function. First, the project will generalize preliminary findings of multi-scale structure within empirical PBINs by developing novel network theories that can be applied to quantitative infection data. Properties of marine PBINs will be analyzed to assess whether they are hierarchically organized, organized into modules, and/or possess multi-scale structure. The statistical structure of PBINs will be integrated with multi-scale coevolutionary models. These co- evolutionary models will be utilized to evaluate hypotheses regarding how cross-infection structure affects community stability. Finally, these coevolutionary models will be used to consider carbon and nutrient regeneration via viral lysis of bacterial hosts. PBIN structure will be varied to establish a link between cross-infection and key indices of ecosystem structure and function, with specific applications to Roseobacter and Synechococcus hosts. Analytical methods and large-scale simulations will be utilized to achieve these goals, closely linked to empirical datasets.Broader impacts: Educational objectives will be centered around the theme of fostering the next generation of quantitative biologists interested in microbial systems (Aim 4). In doing so, the PI will: (i) provide a training program for quantitative biologists that enables them to have direct interactions with students of different backgrounds; (ii) introduce a new course focusing on quantitative viral ecology; (iii) develop and disseminate software tools that enable biologists to apply rigorous quantitative methods to viral-host interaction data and to the study of viral-host communities. Two graduate students and eight undergraduates will be directly supported on this project. These students will have academic backgrounds spanning physics to biology and work in collaborative teams. The graduate students will travel for extended visits to the viral ecology laboratories at the U of Arizona and U of Tennessee-Knoxville. A new graduate course will be developed on quantitative viral ecology to serve trainees on this grant and the growing number of students interested in environmental microbiology at Georgia Tech. The theories developed in this project will be implemented and disseminated as open-source software tools.