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Proportional stochastic generalized Lotka–Volterra model with an application to learning microbial community structures

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  • Xu, Libai
  • Kong, Dehan
  • Wang, Lidan
  • Gu, Hong
  • Kenney, Toby
  • Xu, Ximing

Abstract

Inferring microbial community structure based on temporal metagenomics data is an important goal in microbiome studies. The deterministic generalized Lotka–Volterra (GLV) differential equations have been commonly used to model the dynamics of microbial taxa. However, these approaches fail to take random environmental fluctuations into account and usually ignore the compositional nature of relative abundance data, which may deteriorate the estimates. In this article, we consider the microbial dynamics in terms of relative abundances by introducing a reference taxon, and propose a new proportional stochastic GLV (pSGLV) differential equation model, where the random perturbations of Brownian motion in this model can naturally account for the external environmental effects on the microbial community. We establish conditions and show some mathematical properties of the solutions including general existence and uniqueness, stochastic ultimate boundedness, stochastic permanence, the existence of stationary distribution, and ergodicity property. We further develop approximate maximum likelihood estimators (AMLEs) based on discrete observations and systematically investigate the consistency and asymptotic normality of the proposed estimators. At last, numerical simulations support our theoretical findings and our method is demonstrated through an application to the well-known “moving picture” temporal microbial dataset.

Suggested Citation

  • Xu, Libai & Kong, Dehan & Wang, Lidan & Gu, Hong & Kenney, Toby & Xu, Ximing, 2023. "Proportional stochastic generalized Lotka–Volterra model with an application to learning microbial community structures," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    • Handle: RePEc:eee:apmaco:v:448:y:2023:i:c:s0096300323001017
    DOI: 10.1016/j.amc.2023.127932
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