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Updated: Sep 18, 2025
An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota
Published on: July 31, 2019
Predicting gut microbiota dynamics in obese individuals from cross-sectional data
Ena Melvan1,2, Andrew P Allen1, Tea Vuckovic2
1School of Natural Sciences, Macquarie University, Sydney, NSW, Australia.
Introduction:
Obesity affects approximately 39% of adults worldwide. While gut microbiota has been linked to obesity, most research has focused on static taxonomic composition rather than the dynamic interactions between microbial taxa.
Methods:
We applied BEEM-Static, a generalized Lotka-Volterra model, to cross-sectional 16S rRNA gut microbiome data from six public datasets, comprising 2,435 profiles from lean and obese individuals.
Results:
A total of 57 significant microbial interactions were identified in obese individuals (79% negative), compared to 37 in lean individuals (92% negative). For example, Bacteroidetes showed a stronger inhibitory effect on Firmicutes in obese individuals (-0.41) than in lean ones (-0.26). Firmicutes and Proteobacteria exhibited consistently higher carrying capacities in obese populations.
Discussion:
These findings suggest that microbial interaction networks-not just taxonomic abundance-play a key role in obesity-related dysbiosis. Our approach enables the inference of microbiota dynamics from a single time point, paving the way for tailored dietary interventions, which we refer to as Optibiomics.
