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Geomicrobial kinetics: a genome-scale metabolic modeling approach

地微生物动力学：基因组规模的代谢建模方法

基本信息

批准号：
1636815
负责人：
Qusheng Jin
金额：
$ 24.42万
依托单位：
University of Oregon Eugene
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636815&HistoricalAwards=false
关键词：
Geomicrobial kinetics genome scale metabolic

项目摘要

The investigator proposes to develop a new modeling method to address a compelling question in geobiology - how fast do microbial processes proceed in natural environments? Current methods predict microbial rates in the environment using Monod equation and other models of laboratory cultures. But unlike cultures grown in synthetic media, natural microbes face the challenge of limited and fluctuating availability of nutrients, and thus have to regulate biochemical pathways and to fine-tune the rate and efficiency of metabolism. To bring metabolic pathways and regulations into the prediction of microbial rates, the investigator will develop a new modeling method that explicitly links microbial genomics to geochemistry. This modeling approach would allow to answer the question - how fast do microbial processes proceed in natural environments? The project offers learning opportunities to high school students and supports one Ph.D. and two undergraduate students.The new method groups enzyme reactions within entire cells into two networks of catabolism and biosynthesis. It simulates the catabolic network using enzyme kinetics and biosynthesis network using flux balance analysis. The output of the simulation ranges from enzyme and metabolite concentrations at cellular level to microbial rates at organismal level. The investigator will apply the new method, coupled to biogeochemical reaction modeling, to simulate the metabolism of acetoclastic methanogens, a representative group of subsurface microbes, and to investigate the kinetics of methanogenesis in geological environments. The new method represents an important attempt to move beyond classical Monod equation-based models, toward an omics-enabled metabolism-based approach to the analysis and prediction of microbial processes in geological environments. Specifically, the new method predicts microbial kinetics and enzyme expression from genome and geochemistry of the environment, without the need of pure culture or laboratory experiment. This method therefore offers a new, and potentially powerful, method for studying microbial processes of geological environments, especially those cryptic processes that cannot be accurately analyzed in the field or faithfully reconstructed in the laboratory.

研究人员建议开发一种新的建模方法来解决地球生物学中一个引人注目的问题-微生物过程在自然环境中进行得有多快？目前的方法使用莫诺方程和其他实验室培养模型预测环境中的微生物速率。但与在合成培养基中生长的培养物不同，天然微生物面临着营养物质有限和波动的挑战，因此必须调节生化途径并微调代谢的速率和效率。为了将代谢途径和法规纳入微生物速率的预测，研究人员将开发一种新的建模方法，将微生物基因组学与地球化学明确联系起来。这种建模方法将允许回答这个问题-微生物过程在自然环境中进行得有多快？该项目为高中生提供学习机会，并资助一名博士。新方法将整个细胞内的酶反应分为催化和生物合成两个网络。它使用酶动力学模拟分解代谢网络，使用通量平衡分析模拟生物合成网络。模拟的输出范围从细胞水平的酶和代谢物浓度到生物体水平的微生物速率。研究人员将应用新方法，再加上地球化学反应建模，模拟乙酸分解产甲烷菌的代谢，一组代表性的地下微生物，并研究地质环境中的产甲烷动力学。新方法代表了超越经典的莫诺方程为基础的模型，朝着一个组学启用代谢为基础的方法来分析和预测地质环境中的微生物过程的重要尝试。具体而言，新方法从基因组和环境的地球化学预测微生物动力学和酶表达，而不需要纯培养或实验室实验。因此，这种方法提供了一种新的，潜在的强大的，用于研究地质环境的微生物过程，特别是那些神秘的过程，不能在现场准确分析或忠实地重建在实验室中。