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

智力成就基因测序技术的最新突破为对所有微生物生命的前所未有的广泛了解奠定了基础。此前，该团队已经开发并应用了用于创建基因组尺度代谢模型（mm）的自动化方法。这些方法已经在免费的在线软件系统中应用于数以千计的微生物基因组。虽然mm正在对微生物代谢产生前所未有的见解，但下一个巨大的挑战是捕获基因调控信息，以便更准确地模拟生物体对其环境的代谢反应。预计到这一需求，该小组正在进行的努力为将广泛的替代数据源与数千个mm集成在一起奠定了基础。该小组现在具有独特的优势，可以通过将调控信息整合到模型中来开发增强型mm，从而产生综合代谢调节模型（imrm）。迄今为止，很少有方法学上的努力是针对imrm的大规模发展，部分原因是缺乏对大多数生物体的充分和综合的数据。该项目将(1)通过解决当前方法的方法论缺陷，开发新的和改进的imrm；(2)开发利用imrm预测湿实验室实验条件的方法，以生成和测试新的生物学假设；(3)开发使用数千种模型的新方法，以更好地了解代谢和调节多样性；(4)充分吸收本科生和高中生参与研究的各个方面。因此，该项目将通过开发和评估整合调控数据的方法，大大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的现有基因表达数据集的多样性，通过有针对性的湿实验室实验验证提出的方法，并解决有关微生物生命树的代谢和调控多样性的基本问题。更广泛的影响所有方法上的进步将被整合到一个开源软件环境中，用于模拟微生物的生命。至少21名本科生和至少60名高中生将全面参与这项研究，为他们提供预测系统生物学定量方法的跨学科领域的培训、经验和接触。

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.