机器学习驱动的代谢平衡的优化,用于β-胡卜素生产
Weiming Tu1, Jiabao Xu2, Yongshuo Ma3
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA; School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, Singapore, 639798, Singapore.
Metabolic engineering
|December 13, 2025
概括
这项研究开发了一种机器学习框架,以优化酵母中的β-胡卜素生产. 工程酵母实现了比塔胡卜素的7倍增加,证明了改善了生物产品合成的代谢途径平衡.
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:
- 平衡代谢途径对于有效的微生物生产有价值的化合物至关重要.
- 在为β-胡卜素合成而设计的Yarrowia lipolytica中,脂质合成与胡卜素生产竞争细胞资源.
- 为了获得最佳的生物产品产量,需要精确调节资源分配.
研究的目的:
- 建立一个机器学习框架,以了解和优化Yarrowia lipolytica中的β-胡卜素生产.
- 为了捕捉美酸盐路径,脂质合成和β-胡卜素合成模块之间的复杂相互作用.
- 为了指导代基因整合增强的β-胡卜素标位.
主要方法:
- 开发一个机器学习框架来建模代谢相互作用.
- 基于基因组合的β-胡卜素输出的预测.
- 由计算预测指导的代基因整合.
主要成果:
- 机器学习框架成功模拟了关键代谢模块之间的相互作用.
- 计算指导导致了代基因整合策略.
- 与初始菌株 (YLT001) 相比,表现最好的菌株 (YLT226) 的β-胡卜素标位增加了7倍.
结论:
- 开发的机器学习框架为设计代谢流量分布提供了一个强大的策略.
- 这种方法可以精确调节资源分配,以提高生物产品的合成.
- 这项研究展示了一种优化微生物平台中β-胡卜素生产的成功方法.
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