Transition: Metabolomics-driven understanding of rules that coordinate metabolic responses and adaptive evolution of synthetic biology chassis
Transition: Metabolomics-driven understanding of rules that coordinate metabolic responses and adaptive evolution of synthetic biology chassis
批准号:
2320104
负责人:
Yinjie Tang
金额:
$75.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-15 至 2026-07-31
中文摘要
该过渡奖支持首席研究员接受现代代谢组学和工程生物学方法的培训,使他的实验室能够破译和控制微生物代谢。微生物代谢分配碳和能量资源,以支持生物质生长和生物生产。在合成生物学应用中,异种生物合成的实施会破坏细胞供应链,给微生物带来代谢负担。这一问题导致生物技术中使用的微生物种群发生不可预测的生理变化,为合成生物学微生物菌株的商业化设置了障碍。到目前为止,对代谢负荷如何影响代谢物产生、酶反应速率和细胞适应性的理解仍然很差。随着这一问题的关注,PI正在学习和应用新技术,以揭示导致微生物细胞代谢或进化变化的代谢负担的潜在原因、程度和影响,这些变化被用于生物技术。该项目提供了关于压力条件下微生物生命规则的新知识,并为构建高性能生物制造工作马铺平了道路。此外,该项目培养研究生,本科生和高中生跨学科的生物技术研究。首席研究员正在与林肯大学(一所历史悠久的黑人学院和大学)合作,组织夏季研究和劳动力发展合作。该奖项使首席研究员能够将其研究重点转移到综合代谢组学,工程生物学和生物制造界面的新领域。该专业发展有两个目标,第一个目标是学习现代液相色谱-质谱(LC-MS)技术,该技术可用于执行同位素辅助代谢组学,以发现新的代谢物,描绘功能途径,确定酶反应热力学,并破译基因型-表型关系。第二个目标是获得基于crispr的基因编辑工具,可用于重新编程调节结构和代谢途径。本研究采用新技术,结合碳-13代谢通量分析,分析了一种工业酵母模型(Saccharomyces cerevisiae)在不同生物反应器条件下的天然产物生物合成。所进行的系统分析提高了对酵素反应的驱动力、代谢物抑制和酵母细胞在代谢应激下的适应性细胞反应的理解。新的见解和原理有望促进有效生物生产的微生物途径的设计和控制。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
This Transitions award supports the principal investigator to be trained in modern metabolomics and engineering biology methods, enabling his lab to decipher and control microbial metabolism. Microbial metabolism distributes carbon and energy resources to support biomass growth and bio-productions. During synthetic biology applications, the implementation of heterologous biosynthesis can disrupt the cellular supply chains and impose metabolic burdens on microorganisms. This issue causes microbial populations used in biotechnology, to undergo unpredictable physiological changes, posing a roadblock for commercializing synthetic biology microbial strains. Until now, the understanding of how metabolic burdens affect metabolite productions, enzyme reaction rates, and cellular adaptations is still poor. With this problem in focus, the PI is learning and applying new technologies to uncover the underlying causes, extent, and effects of metabolic burdens that lead to metabolic or evolutionary changes in microbial cells that are used in biotechnology. This project delivers new knowledge on the rules of microbial life under stressed conditions and paves the way for building high performance biomanufacturing workhorses. Moreover, the project trains graduate students, undergraduates, and high school students in interdisciplinary biotechnology research. The principal investigator is partnering with Lincoln University, a Historically Black College and University, to organize both summer research and workforce development collaborations. This award enables the principal investigator to transition the focus of his research to a new area at the interface of integrative metabolomics, engineering biology, and biomanufacturing. The professional development that is pursued has two aims, the first of which is learning modern liquid chromatography–mass spectrometry (LC-MS) techniques that can be used to perform isotope assisted metabolomics to discover new metabolites, delineate functional pathways, determine enzyme reaction thermodynamics, and decipher genotype-phenotype relations. The second aim is to acquire CRISPR-based gene editing tools that can be used to re-program regulatory architecture and metabolic pathways. The research undertaken employs the new techniques, along with carbon-13 metabolic flux analysis, to analyze a model industrial yeast chassis (Saccharomyces cerevisiae) for the biosynthesis of natural products under various bioreactor conditions. The systems analyses undertaken improve the understanding of driving forces of enzyme reactions, metabolite inhibitions, and adaptive cellular responses when yeast cells are under metabolic stresses. The new insights and principles are expected to facilitate the design and control of microbial pathways for effective bioproduction.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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会议论文
URoL:EN: A non-parametric framework to understand emergent behaviors of microbial consortia
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批准号:2222403
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项目类别:Standard Grant
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资助金额:$294.63万
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财政年份:2022
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负责人:Yinjie Tang
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依托单位:
Development of a machine learning pipeline for assisting strain design of nonmodel yeasts
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批准号:2225809
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项目类别:Standard Grant
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资助金额:$94.33万
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财政年份:2022
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负责人:Yinjie Tang
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依托单位:
EAGER: Collaborative Research: Integrating microtome sectioning with isotopic tracing to study biotransformation in synthetic Escherichia coli biofilms
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批准号:1700881
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项目类别:Standard Grant
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资助金额:$3.25万
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财政年份:2017
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负责人:Yinjie Tang
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依托单位:
Collaborative Research: Productivity Prediction of Microbial Cell Factories using Machine Learning and Knowledge Engineering
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批准号:1616619
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项目类别:Standard Grant
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资助金额:$24.55万
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财政年份:2016
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负责人:Yinjie Tang
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依托单位:
Collaborative Research: Use of 13C-labeling and flux modeling to analyze metabolic reactions and gas-liquid mass transfer during syngas fermentations
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批准号:1438125
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项目类别:Standard Grant
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资助金额:$15.0万
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财政年份:2014
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负责人:Yinjie Tang
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依托单位:
ABI innovation: Integration of flux balance analyses with data mining and 13C-labeling experiments to decipher microbial metabolisms
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批准号:1356669
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项目类别:Standard Grant
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资助金额:$48.65万
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财政年份:2014
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负责人:Yinjie Tang
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依托单位:
CAREER: Development of 13C-assisted Metabolic Flux Analysis Tools for Metabolic Engineering of Cyanobacteria
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批准号:0954016
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项目类别:Continuing Grant
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资助金额:$50.0万
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财政年份:2010
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负责人:Yinjie Tang
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依托单位:
海外基金