使用MDSINE2从微生物组数据中学习生态系统规模的动态
Travis E Gibson1,2,3,4, Younhun Kim5,6,7, Sawal Acharya5
1Division of Computational Pathology, Brigham and Women's Hospital, Boston, MA, USA. tegibson@bwh.harvard.edu.
Nature microbiology
|September 9, 2025
概括
我们开发了微生物动态系统推理引擎2 (MDSINE2),这是贝叶斯的方法,可以从时间序列数据中创建微生物生态系统的可解释模型. MDSINE2提供了对肠道微生物群相互作用的见解,优于现有的方法.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 动态系统模型对于分析微生物生态系统非常有价值.
- 从复杂的微生物组数据中学习和解释这些模型带来了重大挑战.
研究的目的:
- 介绍微生物动态系统推理引擎2 (MDSINE2),这是一个贝叶斯的方法学习紧和可解释的生态系统规模的动态系统模型.
- 解决微生物组时间序列数据分析和解释模型输出方面的挑战.
主要方法:
- MDSINE2将微生物动态模型作为交互模块驱动的随机过程.
- 模型包含数据的噪声特征.
- 开发了一套开源软件包,其中包含用于模型解释的工具 (模块的族系/分类学,稳定性,交互拓,关键性).
主要成果:
- MDSINE2是使用微生物组时间序列数据从被干扰的小鼠队列进行基准测试的.
- 在学习动态系统模型方面,MDSINE2的性能优于最先进的方法.
- 确定了交互模块,提供了对肠道微生物群生态系统规模交互的见解.
结论:
- MDSINE2为建模微生物生态系统动态提供了一种强大且易于解释的方法.
- 该方法增强了对肠道微生物群相互作用和微生物群体行为的理解.
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