基于构成数据的微生物相互作用的量化,使用代方法来解决一般化的Lotka-Volterra方程
Yue Huang1, Tianqi Tang2, Xiaowu Dai3
1Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America.
PLoS computational biology
|November 7, 2025
概括
一个新的代Lotka-Volterra (iLV) 模型使用相对丰度数据准确估计微生物相互作用. 这种方法克服了传统模型的局限性,改善了复杂微生物群落的预测.
科学领域:
- 生态生态学 生态生态学
- 微生物学 微生物学
- 计算生物学 计算生物学
背景情况:
- 微生物相互作用对于社区动态,稳定性和宿主健康至关重要.
- 通用洛特卡-沃尔特拉 (gLV) 模型是研究动态的标准,但需要绝对丰度数据,通常在微生物组研究中无法获得.
研究的目的:
- 为构成微生物组数据设计了一个新的代Lotka-Volterra (iLV) 模型.
- 为了应对使用相对丰度估计微生物相互作用的挑战.
主要方法:
- 开发了一种代的Lotka-Volterra (iLV) 模型,适应gLV框架用于组成数据.
- 实施了一种代优化策略,用于参数估计的线性近似和非线性改进.
- 通过模拟和现实数据集 (狮子,斯泰洛尼基亚,奶酪微生物) 验证了iLV模型.
主要成果:
- 在恢复相互作用系数方面,iLV模型在现有方法 (cLV,gLV) 上表现优越.
- iLV准确地预测了不同噪音水平和时间分辨率的物种轨迹.
- 应用显示了预测和观察到的相对丰度之间的一致性,证实了准确性和稳定性.
结论:
- iLV模型有效地将理论的gLV模型与实际的组成数据分析相结合.
- 它提供了一个强大的框架来推断微生物相互作用,并从相对丰度数据中预测社区动态.
- 这一进步对微生物生态学和研究具有重大潜力.
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