MIM: Elucidating the Rules of Cooperation and Resiliency in Microbial Communities through Stochastic Graph Grammars

水生生态系统中的微生物群落对于维持这些重要环境的弹性至关重要。这些密集的微生物种群具有丰富的物种多样性，并且由于基因复制和水平基因转移等过程，它们的基因组是动态的。尽管由于这些进化过程导致了广泛的基因组通量，但微生物群落合作并保持稳定的相互作用，并且对环境扰动具有弹性。该项目旨在开发一套计算工具，用于破译黄石公园温泉微生物垫或生物膜中管理合作和弹性的规则。这些经过充分研究的环境非常适合研究温度、光照和营养等关键环境参数如何影响微生物种群的多样性、丰度和进化。 将开发新的计算工具来确定管理相关生物过程的规则。了解微生物组如何进化和适应，特别是在环境和扰动方面，对于了解生命的许多方面至关重要。因此，从该项目中获得的结果将对生物学，健康，保护工作以及动植物管理产生重大影响。 此外，这项研究将支持位于斯坦福大学校园的赖斯大学和卡内基科学研究所的博士和本科生的跨学科发展。该项目还将支持夏季REU的发展，重点是生物信息学，生态学，进化和微生物学，还将包括夏季培训的机会，研究生与合作者在法国巴黎巴斯德研究所。这一多学科合作项目提出了一项创新计划，将教育和外联活动结合起来，以取得真实的和持久的影响。开发的所有软件都将在开放源码储存库中提供。该项目的总体目标是了解微生物组如何进化和适应，特别是在环境和扰动方面。为了实现这些目标，研究人员团队将引入图论、形式语言和机器学习交叉领域的方法学进步，以从元基因组测序数据中识别控制基因复制和水平基因转移的规则。在目标1中，该项目将通过基因组组装图构建物种丰度网络和基因组交换网络并将其关联起来。在目标2中，该项目将生成一个随机图语法框架，用于对网络随时间的演化进行建模，并使用深度图生成器揭示新规则。在目标3中，基因组水平的模拟将与目标1和2中确定的基因组事件相结合，以评估这些生物过程及其相关速率的进化益处。最后的项目目标将侧重于通过研究图形语法和网络分析技术来表征合作和弹性。在所有四个项目目标中开发的开源计算方法将在四个不同的宏基因组数据集上进行评估，包括环境和宿主相关的微生物组。最后，各种各样的宏基因组数据集将被用来显示主机相关microbiomes.This奖项反映了NSF的法定使命的计算方法的普遍性，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Microbial communities in aquatic ecosystems are central to maintaining the resiliency of these important environments. These dense microbial populations are rich in species diversity and their genomes are dynamic because of processes such as gene duplication and horizontal gene transfer. Despite extensive genomic flux due to these evolutionary processes, microbial communities cooperate and maintain stable interactions and are resilient to environmental perturbations. This project aims to develop a suite of computational tools for deciphering the rules that govern cooperation and resiliency in hot spring microbial mats, or biofilms, from Yellowstone Park. These well-studied environments are ideal for examining how critical environmental parameters such as temperature, light and nutrients influence the diversity, abundance, and evolution of microbial populations. Novel computational tools will be developed to identify rules that govern relevant biological processes. Understanding how microbiomes evolve and adapt, especially with respect to the environment and to perturbations is crucial to understand many aspects of life. Thus, the results obtained from this project will have a significant impact on biology, health, conservation efforts, and animal and plant management. Furthermore, this research will support the interdisciplinary development of a diverse cohort of PhD and undergraduate students at Rice University and Carnegie Institution for Science located on the Stanford University campus. This project will also support the development of a summer REU focused on bioinformatics, ecology, evolution, and microbiology, and will also include summer training opportunities for graduate students with collaborators at the Pasteur Institute in Paris, France. This collaborative, multidisciplinary project presents an innovative plan for combining education and outreach activities to achieve real and lasting impact. All software developed will be made available on open-source code repositories. The overall goal of the project is to understand how microbiomes evolve and adapt, especially with respect to the environment and to perturbations. To meet these goals, the team of researchers will introduce methodological advances at the intersection of graph theory, formal languages, and machine learning to identify rules governing gene duplication and horizontal gene transfer from metagenomic sequencing data. In Aim 1, the project will construct and correlate species abundance networks with genome exchange networks through genome assembly graphs. In Aim 2, the project will produce a stochastic graph grammar framework for modeling network evolution over time, as well as unveil new rules with deep graph generators. In Aim 3, genome-level simulations will be combined with the genomic events identified in Aims 1 and 2 to assess the evolutionary benefits of these biological processes and their associated rates. The final project aim will focus on characterizing cooperation and resiliency through the study of graph grammars and network analysis techniques. The open-source computational methods developed across all four project aims will be evaluated on four distinct metagenomic datasets, including both environmental and host-associated microbiomes. Finally, a wide variety of metagenomic datasets will be used to show generalizability of the computational approaches to host-associated microbiomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.