MIM: Machine Learning, Systems Modeling, and Experimental Approaches to Understand the Universal Rules of Life of Microbiota Using Marine Time Series Data

了解微生物有机体之间的关系，它们的基因和社区的功能，环境，以及这些关系如何反映生命的普遍规则仍然是微生物学的基本问题。研究小组正在利用宏基因组学数据，整个社区的集体DNA内容，从海洋时间序列来解决这些问题。虽然宏基因组学已经有十多年的历史了，但宏基因组数据集的许多方面仍然需要新的方法来提取有价值的信息。该研究团队将最先进的综合机器学习，系统建模和实验方法应用于现有和新生成的时间序列宏基因组数据，以更好地了解微生物群落中的相互作用网络及其对微生物群落功能的影响，这对理解生态系统中元素的全球循环和能量处理具有重要意义。该提案中开发的机器学习和数学建模工具应该为宏基因组的基础分析提供新的途径。理论和计算工具也将直接使统计和机器学习社区在因果推理以及生态建模方面受益。最终，这些工具将使研究人员能够帮助揭示来自许多不同环境（包括动物和植物中存在的微生物组）的普遍生命规则。该项目将为博士后研究员、研究生、本科生和高中生提供跨学科培训，重点是数据科学、计算机科学、统计学、计算生物学、环境生物学和生态学方面代表性不足的群体。在过去的二十年里，与南加州大学微生物观测站有关的圣佩德罗海洋时间（SPOT）系列收集了不同时间尺度的时间序列标记基因、宏基因组和宏转录组数据（每天、每周、每月和季节性地）跨越海洋中的不同深度、位置和扰动（原始和污染）。利用丰富的时间序列数据，研究团队将开发机器学习、系统建模和实验方法，以了解微生物群落的普遍生活规则。该项目的具体目标是（1）开发机器学习方法，通过宏基因组读取组装和分箱来识别微生物群落中的所有已知或新的微生物以及移动的遗传元件（如病毒和质粒）的宿主，（2）进一步研究具有敲除错误发现控制的格兰杰图形模型，将所得计算工具应用于SPOT数据，以确定已知微生物基因组、宏基因组组装基因组和环境因素之间的因果关系。(3)在根据前两个目标构建的因果网络的基础上，开发驱动生物丰度和群落结构的机械模型，如竞争、交叉喂养、病毒与宿主的相互作用、放牧和物理运输，并开发一个预测框架，以应用于未来的各种生态系统。(4)使用邻近连接实验和微生物群落的动态和新兴特性，实验验证预测的病毒-宿主相互作用。将开发便于使用的软件包，使宏基因组数据分析程序自动化。这项研究的共同资金由生物海洋学和数学生物学项目提供。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Understanding the relationships among microbial organisms, the functioning of their genes and communities, the environment, and how these relationships reflect universal rules of life remain essential problems in microbiology. The team of investigators are leveraging metagenomics data, the collective DNA content of the entire community, from a marine time series to address these questions. Although the science of metagenomics is over a decade old, there are still many aspects of metagenomic datasets that require new approaches to extract valuable information. The research team will apply state-of-the-art integrative machine learning, systems modeling and experimental approaches to existing and newly generated time series metagenomic data to better understand the interaction networks in microbial communities and their impacts on microbial community function, which have major implications for understanding the global cycling of elements and processing of energy in ecosystems. The machine learning and mathematical modeling tools developed in this proposal should provide new avenues for fundamental analysis of metagenomes. The theory and computational tools will also directly benefit both the statistical and machine learning community on causal inference as well as ecological modeling. Ultimately, these tools will enable investigators to help uncover the universal rules of life within microbiomes from many different environments, including those present in animals and plants. The project will provide interdisciplinary training for postdoctoral fellows, graduate, undergraduate and high school students with emphasis on underrepresented groups in data science, computer science, statistics, computational biology, environmental biology and ecology. Software tools developed during the project will be disseminated to the community.Over the past two decades, the San Pedro Ocean Time (SPOT) Series associated with University of Southern California Microbial Observatory has collected time series marker gene, metagenomic, and metatranscriptomic data at different time scales (daily, weekly, monthly, and seasonally) across various depths, locations and perturbations (pristine and polluted) in the ocean. With the rich available time series data, the research team will develop machine learning, systems modeling, and experimental approaches to understand the universal rules of life of microbial communities. The specific aims of this project are to (1) develop machine learning approaches to identify all microbes, known or novel, within the microbial communities and also host of mobile genetic elements, such as viruses and plasmids, through metagenomic read assembly and binning, (2) further investigate the Granger graphical models with knockoff false discovery control, apply the resulting computational tools to the SPOT data to identify causal relationships among the known microbial genomes, metagenome assembled genomes, and environmental factors. (3) based on the causal networks constructed from the first two aims, develop mechanistic models driving organism abundances and community structure, such as competition, cross-feeding, virus-host interactions, grazing and physical transport, and develop a predictive framework for application to diverse and future ecosystems. (4) experimentally validate the predicted virus-host interactions using proximity-ligation experiments and the dynamics and emerging properties of the microbial communities. User-friendly software packages to automate the procedures for analyzing metagenomic data will be developed. Co-funding for this research was provided by the Biological Oceanography and Mathematical Biology programs.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.