在环境和高温下通过机器学习引导的组合促进剂修改优化Saccharomyces cerevisiae中的乙醇生产
Peerapat Khamwachirapithak1, Kittapong Sae-Tang1, Wuttichai Mhuantong1
1National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand.
ACS synthetic biology
|September 8, 2023
概括
机器学习通过微调酵母基因促进器来优化生物乙醇生产. 这种方法提高了乙醇产量,使生物乙醇成为更具成本效益和高效的替代燃料.
科学领域:
- 生物技术是生物技术.
- 代谢工程是代谢工程.
- 合成生物学 合成生物学
背景情况:
- 对可持续燃料的日益增长的需求推动了对生物乙醇的兴趣.
- 高温发酵具有成本效益,但对酵母来说具有挑战性.
- 在高温下,Saccharomyces cerevisiae菌株的发酵能力较差.
研究的目的:
- 使用机器学习方法优化Saccharomyces cerevisiae中的生物乙醇生产.
- 微调内源基因的促进活性,以增强乙醇发酵.
- 开发一个具有成本效益和高效的生物乙醇生产工艺.
主要方法:
- 通过用不同强度的促进剂取代本地促进剂,创造了216种组合酵母菌株.
- 使用XGBoost机器学习模型,对促进剂强度和代谢物数据进行训练.
- 应用了ML指导的工作流程,以优化在高温 (40°C) 上生产乙醇.
主要成果:
- 在30°C时,促进剂替代能提高63%的乙醇产量.
- 在40°C的ML引导优化导致乙醇产量增加7.4%.
- 为酵母工程开发了一个全面的促进剂强度修改图书馆.
结论:
- 机器学习有效地引导酵母菌株优化用于生物乙醇生产.
- 这种方法加速了代谢工程,提高了成本效益.
- 优化的酵母菌株显示出高效,大规模生物乙醇制造的潜力.
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