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: 1716285
• 负责人: Aaron Best
• 金额: $ 48.85万
• 依托单位: Hope College
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1716285&HistoricalAwards=false
• 关键词: 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、社会以及人们与世界互动的方式有着深远的影响。这项研究将通过开发新的计算技术，将大量不同的数据与细菌在不同环境中如何发挥作用的模型结合起来，增进对细菌微生物的理解。 结果应该可以更准确地预测细菌代谢。该项目将培养一批数学、统计学、计算机科学和生命科学领域的本科生骨干，为他们在 STEM 领域的职业生涯做好准备。这项工作的方法学进展将可供微生物学家和工程师使用，帮助他们设计利用微生物代谢反应的新应用和流程。测序技术的突破为基因组规模的微生物生命理解奠定了基础。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。先前的工作已经开发并应用自动化方法来创建基因组规模的代谢模型，并开发了一个强大的贝叶斯统计框架来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的信息纳入代谢模型。需要开展额外的工作将此类代谢模型与广泛的替代数据源（包括转录调控网络）整合起来。为了满足这一需求，该项目将应用计算方法进行模型生成、实验验证和反馈的迭代循环，以生成更好的模型结果。 研究结果预计将为科学界生成和应用 iMRM 建立严格的统计基础。此外，该项目将本科生纳入研究的各个方面，作为主要的研究生。

项目成果

Implementing and evaluating a Gaussian mixture framework for identifying gene function from TnSeq data

实施和评估用于从 TnSeq 数据识别基因功能的高斯混合框架

• 发表时间: 2019-01
• 期刊: Pacific symposium on biocomputing ...
• 作者: Li, K;Chen, R;Lindsey, W;Best, AA;DeJongh, M;Henry, C;Tintle, NL
• 通讯作者: Tintle, NL

KBase: The United States Department of Energy Systems Biology Knowledgebase

KBase：美国能源部系统生物学知识库

• DOI: 10.1038/nbt.4163
• 发表时间: 2018-07-06
• 期刊: Nature Biotechnology
• 影响因子: 46.9
• 作者: Arkin AP;Cottingham RW;Henry CS;Harris NL;Stevens RL;Maslov S;Dehal P;Ware D;Perez F;Canon S;Sneddon MW;Henderson ML;Riehl WJ;Murphy-Olson D;Chan SY;Kamimura RT;Kumari S;Drake MM;Brettin TS;Glass EM;Chivian D;Gunter D;Weston DJ;Allen BH;Baumohl J;Best AA;Bowen B;Brenner SE;Bun CC;Chandonia JM;Chia JM;Colasanti R;Conrad N;Davis JJ;Davison BH;DeJongh M;Devoid S;Dietrich E;Dubchak I;Edirisinghe JN;Fang G;Faria JP;Frybarger PM;Gerlach W;Gerstein M;Greiner A;Gurtowski J;Haun HL;He F;Jain R;Joachimiak MP;Keegan KP;Kondo S;Kumar V;Land ML;Meyer F;Mills M;Novichkov PS;Oh T;Olsen GJ;Olson R;Parrello B;Pasternak S;Pearson E;Poon SS;Price GA;Ramakrishnan S;Ranjan P;Ronald PC;Schatz MC;Seaver SMD;Shukla M;Sutormin RA;Syed MH;Thomason J;Tintle NL;Wang D;Xia F;Yoo H;Yoo S;Yu D
• 通讯作者: Yu D

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

ModelSEED 生物化学数据库，用于整合代谢注释以及植物、真菌和微生物代谢模型的重建、比较和分析

• DOI: 10.1093/nar/gkaa746
• 发表时间: 2021-01-08
• 期刊: Nucleic Acids Research
• 影响因子: 14.9
• 作者: Seaver SMD;Liu F;Zhang Q;Jeffryes J;Faria JP;Edirisinghe JN;Mundy M;Chia N;Noor E;Beber ME;Best AA;DeJongh M;Kimbrel JA;D'haeseleer P;McCorkle SR;Bolton JR;Pearson E;Canon S;Wood-Charlson EM;Cottingham RW;Arkin AP;Henry CS
• 通讯作者: Henry CS