Collaborative Research: RUI: Developing Integrated Metabolic Regulatory Models (iMRMs) for the Investigation of Metabolic and Regulatory Diversity of Sequenced Microbes

智力价值基因测序技术的最新突破为前所未有的对所有微生物生命的广泛理解奠定了基础。此前，该小组已经开发和应用了用于创建基因组规模的代谢模型(MMS)的自动化方法。自那以后，这些方法已经在免费提供的在线软件系统中应用于数千个微生物基因组。虽然MMS对微生物新陈代谢产生了前所未有的见解，但下一个巨大的挑战是捕获基因调控信息，以便更准确地模拟有机体对环境的代谢反应。预见到这一需求，该小组正在进行的努力已经为将各种替代数据源与数千条彩信集成奠定了基础。该集团现在处于独特的地位，可以通过将监管信息整合到模型中来开发增强的MMS，产生集成的代谢、调控模型(IMRM)。到目前为止，由于缺乏关于大多数生物体的充分和综合的数据，很少有方法学上的努力针对广泛的磁共振成像技术的发展。该项目将(1)通过解决当前方法中的方法学缺陷来开发新的和改进的iMRM，(2)开发方法来利用iMRM来预测湿实验室实验的条件，以生成和测试新的生物学假说，(3)开发新的方法来使用数千个模型来更好地了解代谢和调节多样性，以及(4)在研究的所有方面充分吸收本科生和高中生。因此，该项目将通过开发和评估纳入调控数据的方法，极大地提高构建综合代谢调控模型的能力，为研究人员提供工具，以评估其感兴趣的生物体现有基因表达数据集的多样性，通过有针对性的湿法实验室实验来验证所提出的方法，并解决关于微生物生命树中代谢和调控多样性的基本问题。至少21名本科生和至少60名高中生将全面参与这项研究，为他们提供培训、经验和接触预测系统生物学定量方法的跨学科领域。

Intellectual MeritRecent breakthroughs in genetic sequencing technology have set the stage for an unprecedented wide understanding of all microbial life. Previously, automated methods for the creation of genome-scale metabolic models (MMs) have been developed and applied by this group. These methods have since been applied to thousands of microbial genomes in freely available online software systems. While the MMs are yielding unprecedented insights into microbial metabolism, the next great challenge is to capture gene regulatory information in order to more accurately model the metabolic response of an organism to its environment. Anticipating this need, ongoing efforts by the group have set the stage for integrating a wide range of alternative data sources with the thousands of MMs. The group is now uniquely positioned to develop enhanced MMs through the integration of regulatory information into the models, yielding integrated metabolic, regulatory models (iMRMs). To date, little methodological effort has been directed toward the wide-scale development of iMRMs due, in part, to the lack of sufficient and integrated data for most organisms. This project will (1) develop new and improved iMRMs by addressing methodological weaknesses in current approaches, (2) develop methods to utilize iMRMs to predict conditions for wet-lab experiments to generate and test novel biological hypotheses, (3) develop novel approaches to use thousands of models to better understand metabolic and regulatory diversity, and (4) fully incorporate undergraduate and high school students in all aspects of the research. Thus, the project will substantially advance the capacity to construct integrated metabolic regulatory models through the development and evaluation of methods for the incorporation of regulatory data, produce tools for researchers to assess the diversity of existing gene expression data sets for their organisms of interest, validate proposed methods via targeted wet lab experiments, and address fundamental questions about metabolic and regulatory diversity across the microbial tree of life.Broader ImpactsAll methodological advancements will be integrated into an open-source software environment for modeling microbial life. At least 21 undergraduate and at least 60 high school students will be integrally involved in the research, providing them with training, experience and exposure to the interdisciplinary field of quantitative approaches for predictive systems biology.