通过多重实验和机器学习实现路径设计
Aashutosh Girish Boob1, Junyu Chen2, Huimin Zhao3
1Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
Metabolic engineering
|December 1, 2023
概括
本综述探讨了先进的途径工程策略,包括多重实验和机器学习,以克服微生物生产有价值分子的局限性. 这些方法加快了最佳生物合成途径的发现,以提高产量和成本效益.
科学领域:
- 代谢工程和合成生物学用于可持续的化学生产.
背景情况:
- 自然的代谢多样性为有价值的分子合成提供了途径.
- 宿主转移的挑战包括中间积累和竞争途径,阻碍产量.
- 传统的优化方法是缓慢的,劳动密集型的,并且经常产生低于最佳的结果.
研究的目的:
- 审查用于增强分子生产的途径工程方面的进展.
- 突出多重实验和机器学习在优化路径中的作用.
- 讨论可持续微生物生产化学品的工具和策略.
主要方法:
- 对路径工程技术的最新文献的审查.
- 专注于多重实验,探索酶表达场景.
- 机器学习的应用用于识别最佳路径配置.
主要成果:
- 多重体实验和机器学习克服了传统方法的局限性.
- 这些方法使得更广泛的设计空间探索可用于路径优化.
- 发现最佳途径配置以提高分子产量的可能性增加.
结论:
- 多重体实验和机器学习显著改善了传统方法.
- 这些方法对于克服代谢通路工程的局限性至关重要.
- 它们增强了有效的微生物细胞工厂的发现,以实现可持续的化学合成.
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