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Related Experiment Video

Updated: Sep 19, 2025

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

Published on: July 22, 2022

Variance in C. elegans gut bacterial load suggests complex host-microbe dynamics.

Satya Spandana Boddu¹, K Michael Martini^1,2, Ilya Nemenman^1,2,3

¹Department of Physics, Emory University, Atlanta, Georgia, United States of America.

Plos Computational Biology

|June 9, 2025

Summary

This summary is machine-generated.

Microbial community assembly in hosts is complex. Our study reveals bacterial dynamics depend on stochastic switching between high and low growth rates, not just simple models.

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Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans

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Published on: September 13, 2022

Area of Science:

Microbiology
Systems Biology
Computational Biology

Background:

Understanding the factors driving variation in host-associated bacterial communities is crucial but challenging.
Current models of microbial community assembly often simplify complex ecological dynamics.

Purpose of the Study:

To deconstruct the factors contributing to variation in bacterial composition within a host.
To develop and test mathematical models explaining microbial dynamics.

Main Methods:

Combined experimental approaches with mathematical modeling.
Analyzed time-series data of host-associated bacterial communities.
Developed novel stochastic models to capture observed dynamics.

Main Results:

Demographic stochasticity and stationary heterogeneity do not fully explain observed bacterial variation.
Bacterial community dynamics are better explained by stochastic switching between high and low growth rate phenotypes.
Developed mathematical models that quantitatively explain empirical data.

Conclusions:

Microbiome assembly dynamics are more complex than traditional logistic growth models suggest.
Stochastic switching between growth phenotypes is a key factor in host-bacterial interactions.
Time-series data and advanced modeling are essential for understanding microbial dynamics within hosts.