用机器学习在基因组规模上建模宿主通路动态
Charlotte Merzbacher1, Oisin Mac Aodha2, Diego A Oyarzún3
1School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.
Metabolic engineering
|June 24, 2025
概括
这项研究引入了一种新的计算方法,结合了动力学和基因组规模的代谢模型,用于预测工程微生物中的动态代谢行为. 这种方法加速了用于可持续化学品生产的菌株设计.
科学领域:
- 代谢工程是代谢工程.
- 合成生物学 合成生物学
- 计算生物学 计算生物学
背景情况:
- 路径工程是可持续化学生产的关键.
- 预测工程宿主中的动态代谢变化是一项挑战.
- 目前的基因组规模模型缺乏动态模拟能力.
研究的目的:
- 开发一个新的计算框架,整合动力学和基因组规模的代谢模型.
- 为了能够预测发酵期间的动态代谢物和酶行为.
- 改进微生物生产系统的计算菌株设计.
主要方法:
- 集成的运动路径模型与基因组规模的代谢模型.
- 采用替代机器学习模型来加快流量平衡分析 (FBA) 计算.
- 模拟的局部非线性动态由全球宿主代谢状态告知.
主要成果:
- 实现了超过两个数量级的模拟加速.
- 证明了对大肠杆菌中代谢物动态的一致预测.
- 成功预测了遗传干扰和不同碳源的影响.
结论:
- 综合建模方法为计算应变设计提供了一个全面的框架.
- 该方法有助于对动态控制电路和优化策略进行选.
- 这项工作弥合了代谢工程中静态和动态建模之间的差距.
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