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Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized

Yue Huang1, Tianqi Tang2, Xiaowu Dai3

  • 1Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America.

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A new iterative Lotka-Volterra (iLV) model accurately estimates microbial interactions using relative abundance data. This method overcomes limitations of traditional models, improving predictions in complex microbial communities.

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Area of Science:

  • Ecology
  • Microbiology
  • Computational Biology

Background:

  • Microbial interactions are crucial for community dynamics, stability, and host health.
  • Generalized Lotka-Volterra (gLV) models are standard for studying dynamics but require absolute abundance data, often unavailable in microbiome research.

Purpose of the Study:

  • To introduce a novel iterative Lotka-Volterra (iLV) model designed for compositional microbiome data.
  • To address the challenge of estimating microbial interactions using relative abundances.

Main Methods:

  • Developed an iterative Lotka-Volterra (iLV) model adapting the gLV framework for compositional data.
  • Implemented an iterative optimization strategy with linear approximations and nonlinear refinements for parameter estimation.
  • Validated the iLV model using simulations and real-world datasets (lynx-hare, Stylonychia, cheese microbes).

Main Results:

  • The iLV model demonstrated superior performance over existing methods (cLV, gLV) in recovering interaction coefficients.
  • iLV accurately predicted species trajectories across varying noise levels and temporal resolutions.
  • Applications showed consistency between predicted and observed relative abundances, confirming accuracy and robustness.

Conclusions:

  • The iLV model effectively bridges theoretical gLV models with practical compositional data analysis.
  • It offers a robust framework for inferring microbial interactions and predicting community dynamics from relative abundance data.
  • This advancement holds significant potential for microbial ecology and research.