一个白盒机器学习方法来揭示抗生素的作用机制
Jason H Yang1, Sarah N Wright1, Meagan Hamblin2
1Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Cell
|May 14, 2019
概括
我们开发了一种结合生化查,网络建模和机器学习的新方法,
科学领域:
- 生物化学
- 系统生物学
- 机器学习
背景情况:
- 目前的机器学习方法擅长将生物信号与表型联系起来, 但无法确定因果关系.
- 了解因果机制对于推进药物发现和疗效研究至关重要.
研究的目的:
- 开发和应用一种综合方法来确定抗生素功效的潜在因果机制.
- 阐明新陈代谢状态在细菌对抗生素反应中的作用.
主要方法:
- 开发了整合生化查,基因组规模代谢网络建模和机器学习的"白盒"方法.
- 在大肠杆菌中对代谢物进行抗菌抗生素的反查.
- 使用代谢网络模型和对模拟进行回归查数据来模拟代谢状态.
主要成果:
- 鉴定出纯素生物合成是抗生素致死性的关键因素.
- 证明抗生素诱导的腺限制提高了ATP需求和中央碳代谢.
- 显示增加的代谢活动增强了抗生素的杀伤效果.
结论:
- 综合网络建模和机器学习方法可以识别复杂的因果机制.
- 代谢干扰,特别是腺素限制,显著影响抗生素的疗效.
- 这种框架为了解和预测药物反应提供了一条途径.
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