自动化和机器学习推动了 Pseudomonas putida 中异二醇生产的快速优化
David N Carruthers1,2, Patrick C Kinnunen1,2, Yuerong Li2,3
1Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Nature communications
|December 13, 2025
概括
我们使用机器学习和实验室自动化来提高Pseudomonas putida中的异二醇产量五倍. 这种自动化方法有效地优化微生物代谢工程,以获得可持续的燃料前体.
科学领域:
- 代谢工程是代谢工程.
- 合成生物学 合成生物学
- 生物技术是生物技术.
背景情况:
- 微生物代谢工程与产品改进的复杂数据作斗争.
- 优化生物生产需要有效地分析代谢数据集.
- 异二醇是可持续航空燃料的关键前体.
研究的目的:
- 系统地优化Pseudomonas putida中的异二醇产量.
- 将实验室自动化与机器学习结合起来,用于代谢工程.
- 提高微生物生物生产活动中的产品标位.
主要方法:
- 利用CRISPR干扰 (CRISPRi) 来同时降低基因调控.
- 使用机器学习来指导基因标的选择.
- 实现了自动化的设计-构建-测试-学习周期.
- 进行高通量蛋白质组学用于验证和机制识别.
主要成果:
- 实现了异二醇标位的5倍增加.
- 在六个代周期中成功优化了生产.
- 探索了80万个组合,通过机器学习优先考虑了400个构造.
- 验证了CRISPRi下调,并确定了关键的生物机制.
结论:
- 机器学习驱动的自动化循环迅速提高微生物标位.
- 这种方法可以加速生物生产的优化,而不需要深入的生物专业知识.
- 该方法广泛适用于不同的宿主,产品和代谢途径.
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