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

合作研究：RUI：开发综合代谢调节模型（iMRM）用于研究测序微生物的代谢和调节多样性

• 批准号: 1330813
• 负责人: Nathan Tintle
• 金额: $ 24.87万
• 依托单位: Dordt University, Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1330813&HistoricalAwards=false
• 关键词: Collaborative; Research; RUI; Developing; Integrated

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.

遗传测序技术中的知识分子精致突破为对所有微生物生命的空前广泛理解奠定了基础。 以前，该组已经开发并应用了创建基因组规模代谢模型（MMS）的自动化方法。 此后，这些方法已应用于免费可用的在线软件系统中的数千个微生物基因组。 尽管MMS对微生物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节信息，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，小组的持续努力为将广泛的替代数据源与成千上万的MMS整合在一起奠定了基础。 现在，该小组的位置是通过将调节信息集成到模型中，以开发增强的MMS，从而产生综合的代谢，调节模型（IMRMS）。 迄今为止，几乎没有针对大多数生物缺乏足够和集成的数据，几乎没有针对IMRM的广泛发展。 该项目将（1）通过解决当前方法中的方法论弱点来开发新的和改进的IMRM，（2）开发方法来利用IMRM来预测湿与LAB实验的条件，以生成和测试新型的生物学假设，（3）开发新的方法来使用成千上万的模型来更好地理解代理和监管多样性的多样性多样性，并（4）完整的学生和高等教育的学生和高等教育的学生和高等教育，并培养了各个方面。 因此，该项目将通过开发和评估合并监管数据的方法来实质性地提高构建综合代谢调节模型的能力，为研究人员生成工具，以评估研究人员感兴趣的有机体的现有基因表达数据集的多样性，通过目标湿法实验和跨跨越的范围方法来验证所提出的方法，以解决跨越的生命。集成到开源软件环境中，以建模微生物寿命。至少有21名本科生和至少有60名高中生将积极参与研究，从而为预测系统生物学的定量方法提供培训，经验和接触跨学科的领域。