Linking complex microbial interactions and dysbiosis through a disordered Lotka-Volterra model.

Area of Science:

• Microbial Ecology
• Statistical Physics
• Systems Biology

Background:

• Metagenomics advances microbial community study.
• Microbiome composition is vital for ecological functions and host health.
• Understanding community structure, interactions, and stability is key.

Purpose of the Study:

• Develop a theoretical framework linking metagenomic data to ecosystem modeling.
• Define and compare macroecological states using diversity, interactions, and stability.
• Apply disordered system theory to microbiome characterization.

Main Methods:

• Coupled statistical physics tools for disordered systems with metagenomic data.
• Employed the generalized Lotka-Volterra model with random interactions.
• Analyzed species interaction networks and abundance distributions.

Main Results:

• Identified distinct patterns for healthy and diseased microbiomes.
• Healthy microbiomes show similar structures, strong interactions, and neutral fluctuations.
• Diseased microbiomes exhibit greater variability, deterministic factors, and less stability.

Conclusions:

• Disordered system theory offers a novel approach to characterize microbiomes.
• Ecological interactions play a critical role in microbiome stability and function.
• The framework distinguishes healthy from diseased states based on community dynamics.