论文标题
随机广义Lotka-Volterra模型，其应用于学习微生物社区结构
Stochastic Generalized Lotka-Volterra Model with An Application to Learning Microbial Community Structures
论文作者
论文摘要
基于时间宏基因组学数据推断微生物群落结构是微生物组研究中的重要目标。确定性的广义Lotka-volterra差异（GLV）方程已用于模拟微生物数据的动力学。但是，这些方法未能考虑到随机的环境波动，这可能会对估计值产生负面影响。我们提出了一种新的随机GLV（SGLV）微分方程模型，其中该模型中布朗运动的随机扰动自然可以考虑到对微生物群落的外部环境影响。我们建立了新的条件，并显示了解决方案的各种数学特性，包括一般存在和唯一性，固定分布和牙等性。我们基于离散的观察结果进一步开发近似的最大似然估计器，并系统地研究了所提出的估计量的一致性和渐近正态性。我们的方法是通过仿真研究以及对众所周知的“运动图片”时间微生物数据集的应用来证明的。
Inferring microbial community structure based on temporal metagenomics data is an important goal in microbiome studies. The deterministic generalized Lotka-Volterra differential (GLV) equations have been used to model the dynamics of microbial data. However, these approaches fail to take random environmental fluctuations into account, which may negatively impact the estimates. We propose a new stochastic GLV (SGLV) differential equation model, where the random perturbations of Brownian motion in the model can naturally account for the external environmental effects on the microbial community. We establish new conditions and show various mathematical properties of the solutions including general existence and uniqueness, stationary distribution, and ergodicity. We further develop approximate maximum likelihood estimators based on discrete observations and systematically investigate the consistency and asymptotic normality of the proposed estimators. Our method is demonstrated through simulation studies and an application to the well-known "moving picture" temporal microbial dataset.
