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Machine-learning generated nucleases for accelerating the deployment of a novel, low-emission food production systems

Machine-learning generated nucleases for accelerating the deployment of a novel, low-emission food production systems
机器学习生成核酸酶,用于加速新型低排放食品生产系统的部署
批准号:
10072768
负责人:
金额:
$6.37万
依托单位:
依托单位国家:
英国
项目类别:
Grant for R&D
财政年份:
2023
资助国家:
英国
项目状态:
已结题
起止时间:
2023 至 --

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英文摘要
**Eden Bio's proposed tool will support sustainable protein production by leveraging state-of-the-art machine learning technology, 1) enabling food businesses to scale up their activities and 2) accelerating Net Zero initiatives.**Precision fermentation (PF) is a sustainable protein production method that doesn't use animals. PF has tremendous potential and is a focus of significant investments. This method creates functional ingredients by creating "cell factories" in microbes. Enzymes, flavouring agents, proteins, vitamins, natural pigments, and fats can be produced through PF, and the market is growing rapidly (predicted to reach $37.35B by 2030). One of the key drivers of this growth is the unmet global demand for sustainable protein production.A growing number of companies produce proteins (e.g., food-tech startups) that rely on PF, however, they all struggle to produce their products at an industrial scale. This not only presents huge barriers for these companies to reaching revenue generation but also slows down their economic sector's modernisation and transition towards sustainability.PF involves the production of proteins by genetically modifying microorganisms, thus, it is heavily reliant on genome editing tools and nucleases, a type of protein that cuts the genome in a specific place to allow an edit to take place. Currently available nuclease datasets are limited to either very specific, narrowly applicable types or have limited entries, while the use of commercially available nucleases is prohibitively expensive.To continue offering best-in-class strain optimisation services, EB proposes to develop a repository of fully annotated nucleases and an ML tool for generating non-IP infringing nucleases that will be customisable for the clients' specific needs. This tool will add significant value to EB's services and will allow for the acceleration of PF company's scaling and transition to sustainable food production systems.
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  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    Nicola Rosario Napolitano
  • 依托单位:
煤矿安全人机混合群智感知任务的约束动态多目标Q-learning进化分配
  • 批准号:
    --
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30万元
  • 批准年份:
    2022
  • 负责人:
    吉建娇
  • 依托单位:
基于领弹失效考量的智能弹药编队短时在线Q-learning协同控制机理
  • 批准号:
    62003314
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2020
  • 负责人:
    沈剑
  • 依托单位: