强化学习用于表型异质细菌群的适应性控制
Josiah Kratz1, Zihang Wen1, Shiladitya Banerjee2
1Computational Biology Department, Carnegie Mellon University, Pittsburgh, 15213, PA, USA.
bioRxiv : the preprint server for biology
|December 15, 2025
概括
本研究引入了强化学习 (RL) 框架,以创建适应性抗生素治疗策略. 人工智能学会通过推断它们的隐藏状态来控制细菌种群,甚至管理营养的可用性以有效地灭绝细菌.
科学领域:
- 微生物学和计算生物学
- 抗微生物耐药性研究研究
背景情况:
- 细菌种群由于生理异质性而表现出对抗生素的弹性.
- 环境波动加剧了这种异质性,降低了固定剂量治疗的疗效.
- 抗微生物药物耐药性构成了全球健康的重大威胁.
研究的目的:
- 通过强化学习 (RL) 开发适应性治疗策略框架.
- 从人口层面的测量结果推断细菌群体的生理状态.
- 设计数据驱动的策略来打击抗微生物药物耐药性.
主要方法:
- 实施了强化学习 (RL) 框架,用于适应性治疗发现.
- 利用实验上可访问的,人口层面的测量作为输入.
- 将营养物质可用性的综合控制纳入治疗方案.
- 训练RL代理人推断细菌群体隐藏的生理状态.
主要成果:
- RL剂成功推断出隐藏的细菌生理状态.
- 该框架产生了适应性策略,即使在新条件下也有效.
- 与药物动态一起控制营养的可用性导致了持续的人口灭绝.
- RL方法的表现优于仅基于药物动态的适应性协议.
结论:
- 一个新的RL框架可以设计有效的适应性抗生素治疗策略.
- 推断生理状态和控制营养物质的可用性是细菌种群控制的关键.
- 这种数据驱动的方法提供了对抗抗菌素耐药性的强大工具.
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