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Updated: May 11, 2025

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

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Dynamic coexistence driven by physiological transitions in microbial communities.

Avaneesh V Narla¹, Terence Hwa¹, Arvind Murugan²

¹Department of Physics, University of California, San Diego, La Jolla, CA 92093-0319.

Proceedings of the National Academy of Sciences of the United States of America

|April 17, 2025

Summary

This summary is machine-generated.

Microbial communities shift between growth and non-growth states, influencing ecosystem dynamics. The Community State Model reveals how these dynamic physiological states enhance stability and diversity in microbial ecosystems.

Keywords:

bacterial physiology community assembly ecological succession microbial ecology

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Area of Science:

Microbial Ecology
Theoretical Ecology
Systems Biology

Background:

Traditional microbial ecosystem models assume fixed species interactions and steady exponential growth.
Microbial growth often alters environments, leading to transitions from growth to stressed, non-growing states.
These dynamics are observed in natural ecological succession and laboratory serial-dilution experiments.

Purpose of the Study:

To introduce a phenomenological model, the Community State Model (CSM), for understanding microbial coexistence in dynamic, cyclic environments.
To investigate how changes in physiological states during cyclic succession impact microbial community dynamics.
To identify key features of dynamical communities that differ from steady-state communities.

Main Methods:

Developed a phenomenological model (Community State Model) focusing on species' growth preferences along a global ecological coordinate (biomass density).
The model is agnostic to specific interactions, emphasizing self-consistency conditions of physiological states ('community states').
Analyzed the model to identify emergent properties of dynamically coexisting microbial communities.

Main Results:

Dynamical communities exhibit enhanced stability through staggered species dominance across different community states.
Community diversity tolerance increases, with fast-growing species dominating distinct community states.
Late-growing species show an increased requirement for growth dominance in these dynamic systems.

Conclusions:

The Community State Model provides insights into microbial coexistence driven by dynamic physiological states.
Key features of dynamical communities include enhanced stability, increased diversity tolerance, and altered growth dominance patterns.
The model shifts focus from bottom-up interaction studies to top-down analysis of macroscopic observables like growth rates and biomass density.