强化学习为强大的动态代谢控制提供强化学习
Sebastián Espinel-Ríos1, River Walser2, Dongda Zhang3
1Biomedical Manufacturing Program, Commonwealth Scientific and Industrial Research Organisation, Clayton, Victoria, Australia.
Biotechnology and bioengineering
|October 2, 2025
概括
本研究引入了一个强化学习框架,用于生物过程中的动态代谢控制. 它优化了酶表达,提高了灵活性和可重复性,克服了复杂生物系统中的挑战.
科学领域:
- 生物技术是生物技术.
- 代谢工程是代谢工程.
- 控制系统 控制系统
背景情况:
- 动态代谢控制通过实时调节代谢流量来提高生物过程的灵活性.
- 优化控制策略是复杂的,因为高维空间,代谢负担,和随机动态.
研究的目的:
- 开发一个强化学习 (RL) 框架来推导最佳的动态代谢控制政策.
- 通过域随机化来提高生物过程的稳定性和普遍性,跨越不确定性.
主要方法:
- 实现了一个强化学习代理与代用动态模型交互.
- 应用域随机化来提高政策针对不确定性的稳定性.
- 利用前模型集成,与传统基于模型的方法相比,简化控制.
主要成果:
- 在两个大肠杆菌生物过程中证明了框架的有效性.
- 成功地应用了脂肪酸合成 (乙-CoA碳氧化酶) 和乳酸合成 (腺三酸酶) 的动态控制.
结论:
- 在复杂的生物过程中,RL框架为传统的控制方法提供了强大的替代方案.
- 该方法通过避免模型差异化并依赖前集成来简化控制任务.
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