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Synthetic biology approach to rewire carbon metabolism for efficient photoautotrophic chemical production

Synthetic biology approach to rewire carbon metabolism for efficient photoautotrophic chemical production
合成生物学方法重新连接碳代谢以实现高效的光自养化学生产
批准号:
1902014
负责人:
Shota Atsumi
金额:
$41.82万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-07-01 至 2024-06-30

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中文摘要
翻译
气候变化刺激了减少二氧化碳净排放量的努力。为了解决这个问题,将对光合作用微生物进行改造,使其循环二氧化碳以生产生物燃料或高价值化学品。本科生和研究生将参与这个项目。他们将对生物化学、微生物学和生物工程之间的联系有更深入的了解。项目成果将与高中和大学的科学和工程教育相结合。辅导将扩展到高中生,包括那些来自代表不足的群体的学生。这将为他们提供在学术环境中进行研究的机会。该项目将评估一项改善二氧化碳固定的战略,该战略涉及重新连接蓝藻中的碳代谢。蓝藻可以直接将太阳能和二氧化碳转化为生物燃料和生物产品。工程蓝藻可以生产各种化合物,但生产率太低,不具有商业吸引力。此外,目前的大规模光合作用生产计划依赖于自然阳光提供能量,从而将生产限制在白天。拟议的生产系统就是为了克服这些限制而设计的。此前已有研究表明,CO2固定酶Rubisco的改进具有很大的挑战性。因此,该项目将开发一种替代战略,通过增加Rubisco的细胞内底物水平和引导碳通量用于化学生产来改善碳固定。开发的策略也将作为探测和理解光营养行为和新陈代谢的工具。这种方法解决了有关蓝藻化学生产的商业可行性所提出的许多问题。拟议的工程蓝藻从二氧化碳中制造化学品有可能导致更高效、更经济和更可控的生产系统。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势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
Climate change has spurred efforts to reduce net carbon dioxide (CO2) emissions. To address this issue, photosynthetic microorganisms will be engineered to recycle CO2 to produce biofuels or high-value chemicals. Undergraduate and graduate students will participate in this project. They will gain perspective on connections between biochemistry, microbiology, and bioengineering. The project results will be integrated with science and engineering education at the high school and college levels. Mentoring will be extended to high school students, including those from underrepresented groups. This will provide them with the opportunity to perform research in an academic setting. This project will evaluate a strategy to improve CO2 fixation that involves rewiring carbon metabolism in cyanobacteria. Cyanobacteria can directly convert solar energy and CO2 to biofuels and bioproducts. Engineered cyanobacteria can produce a variety of compounds, but the productivities are too low to be commercially appealing. Also, current large-scale photosynthetic production schemes rely on natural sunlight for energy, thereby limiting production to daylight hours. The proposed production system is designed to overcome these limitations. It has been shown previously that improvement of RuBisCO, the CO2 fixation enzyme, is very challenging. Thus, this project will develop an alternative strategy to improve carbon fixation by increasing intracellular levels of the substrate for RuBisCO and directing carbon flux towards chemical production. The strategies developed will also serve as tools for probing and understanding phototrophic behavior and metabolism. This approach addresses many of the issues raised concerning the commercial feasibility of cyanobacterial chemical production. The proposed engineering cyanobacteria to build chemicals from CO2 has the potential to result in more efficient, economical, and controllable production systems.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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CAREER: Development of a platform for cyanobacterial chemical production from CO2
  • 批准号:
    1349663
  • 项目类别:
    Standard Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2014
  • 负责人:
    Shota Atsumi
  • 依托单位:
Trophic Conversion of an Obligate Photoautotrophic Organism to Produce Isobutyraldehyde in the Absence of Light
  • 批准号:
    1132442
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2011
  • 负责人:
    Shota Atsumi
  • 依托单位:
Synthetic biology approaches toward direct recycling of carbon dioxide to C4-C5 alcohols in thermophilic cyanobacteria
  • 批准号:
    1066182
  • 项目类别:
    Standard Grant
  • 资助金额:
    $36.14万
  • 财政年份:
    2011
  • 负责人:
    Shota Atsumi
  • 依托单位:
国内基金
海外基金
组蛋白乙酰化修饰ATG13激活自噬在牵张应力介导骨缝Gli1+干细胞成骨中的机制研究
  • 批准号:
    82370988
  • 项目类别:
    面上项目
  • 资助金额:
    48.00万元
  • 批准年份:
    2023
  • 负责人:
    经典
  • 依托单位:
Journal of Integrative Plant Biology
  • 批准号:
    31024801
  • 项目类别:
    专项基金项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2010
  • 负责人:
    贺萍
  • 依托单位:
Computational Methods for Analyzing Toponome Data