Collaborative Research: RUI: Investigating microbial metabolic and regulatory diversity by modeling gene activity states inferred from transcriptome data

合作研究：RUI：通过对转录组数据推断的基因活性状态进行建模来研究微生物代谢和调控多样性

• 批准号: 1715211
• 负责人: Nathan Tintle
• 金额: $ 27.12万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1715211&HistoricalAwards=false
• 关键词: Collaborative; Research; RUI; Investigating; microbial; Collaborative; Research; RUI; Investigating; microbial

Microorganisms have profound impacts on the environment, society and the ways that people interact with the world. This research will advance understanding of bacterial microorganisms by developing new computational technologies for combining very large sets of diverse data with models of how bacteria function in different environments. The results should allow for more accurate prediction of bacterial metabolism. The project will train a cadre of undergraduate students in mathematics, statistics, computer science, and life science and prepare them for careers in STEM fields. The methodological advancements from this work will be available for use by microbiologists and engineers to help them design new applications and processes that make use of microbial metabolic reactions. Breakthroughs in sequencing technology have set the stage for genome-scale understanding of microbial life. 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. Prior work has developed and applied automated methods to create genome-scale metabolic models and developed a robust Bayesian statistical framework for estimating gene activity states of bacteria. These estimates are used as constraints in an integrated metabolic and regulatory model (iMRM), effectively incorporating information from large scale expression data into metabolic models. Additional work is needed to integrate such metabolic models with a wide range of alternative data sources, including transcriptional regulatory networks. To meet this need, this project will apply computational approaches for iterative cycles of model generation, experimental validation and feedback to generate better model outcomes. The results are expected to establish a rigorous statistical foundation for the generation and application of iMRMs by the scientific community. Further, the project incorporates undergraduate students in all aspects of the research as the primary research students.

微生物对环境，社会和人们与世界互动的方式产生了深远的影响。这项研究将通过开发新的计算技术来提高对细菌微生物的了解，以将非常大的不同数据与细菌如何在不同环境中发挥作用的模型相结合。 结果应允许更准确地预测细菌代谢。该项目将培训数学，统计学，计算机科学和生命科学的本科生的干部，并为STEM领域的职业做好准备。这项工作的方法学进步将可供微生物学家和工程师使用，以帮助他们设计使用微生物代谢反应的新应用和过程。测序技术中的突破为基因组规模了解微生物寿命奠定了基础。下一个巨大的挑战是捕获基因调节信息，以便更准确地对生物体对环境的代谢反应进行建模。先前的工作已经开发并应用了自动化方法来创建基因组规模的代谢模型，并开发了一种强大的贝叶斯统计框架，用于估计细菌的基因活性状态。这些估计值用作综合代谢和调节模型（IMRM）的约束，有效地将大规模表达数据中的信息纳入了代谢模型中。需要其他工作来将这些代谢模型与包括转录调节网络在内的各种替代数据源集成。为了满足这一需求，该项目将应用计算方法，以实现模型生成，实验验证和反馈的迭代周期，以产生更好的模型结果。 结果预计将为科学界建立严格的IMRM生成和应用的统计基础。此外，该项目将本科生在研究的各个方面都作为主要研究专业的学生。