Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

What are Populations and Communities?

What are Populations and Communities?

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

Bacterial Signaling

Bacterial Signaling

Bacterial signaling can occur within bacteria (intracellular) or between bacteria (intercellular). At times, a group of bacteria behaves like a community. To achieve this, they engage in quorum sensing, the perception of higher cell density that causes changes in gene expression. Quorum sensing involves both extracellular and intracellular signaling. The signaling cascade starts with a molecule called an autoinducer (AI). Individual bacteria produce AIs that move out of the bacterial cell...

Speciation Rates

Speciation Rates

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Combinatorial decision-making driven by multicomponent surface condensates.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Single-cell heterogeneity in ribosome levels and protein synthesis during nutrient starvation is driven by cAMP signaling.

Science advances·2026

Same author

Minimizing co-growth as a broad predictor of community robustness.

bioRxiv : the preprint server for biology·2026

Same author

Simple biological controllers drive the evolution of soft modes.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Evolution of error correction through a need for speed.

Science (New York, N.Y.)·2026

Same author

Bacterial iron acquisition by <i>Escherichia coli</i> is facilitated by amino acid complexation in a rapid-renewal environment.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same journal

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes.

ArXiv·2026

Same journal

A Positron Range Correction with Texture Preservation Framework in PET Imaging.

ArXiv·2026

Same journal

Automated optimization of force field parameters against ensemble-averaged measurements with Bayesian Inference of Conformational Populations.

ArXiv·2026

Same journal

Droplet Fusion as a Relaxation Process: Comparison with Shape Recovery of Newtonian and Viscoelastic Droplets.

ArXiv·2026

Same journal

Ridge-filter crosstalk in conformal proton FLASH planning: dependence on beamlet pitch and iterative mitigation.

ArXiv·2026

Same journal

Electrochemical DNA Hairpin Sensors for Differentiating Small Molecule Intercalation from Minor Groove Binding.

ArXiv·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 5, 2025

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

Published on: August 5, 2020

Dynamic coexistence driven by physiological transitions in microbial communities.

Avaneesh V Narla¹, Terence Hwa¹, Arvind Murugan²

¹Department of Physics, University of California, San Diego.

|January 23, 2024

Summary

This summary is machine-generated.

Microbial communities can coexist dynamically by shifting between growth states, not just fixed interactions. This new model reveals how biomass density influences species diversity and stability in microbial ecosystems.

More Related Videos

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection

Published on: November 18, 2022

Related Experiment Videos

Last Updated: Jul 5, 2025

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

Published on: August 5, 2020

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection

Published on: November 18, 2022

Area of Science:

Microbial Ecology
Theoretical Ecology
Systems Biology

Background:

Microbial ecosystems are often modeled using fixed species interactions under steady exponential growth.
Microbes significantly alter their environment, leading to non-growing or stressed states, typical in ecological succession and lab cultures.
Existing models often overlook the impact of dynamic physiological state changes on microbial community structure.

Purpose of the Study:

To introduce a phenomenological model, the Community State model, for understanding microbial coexistence driven by dynamic physiological state changes.
To investigate how microbial communities transition between different physiological states and how this impacts coexistence.
To identify key features of dynamical microbial communities that differ from steady-state models.

Main Methods:

Developed a phenomenological model (Community State model) focusing on growth preference along a global ecological coordinate (total community biomass density).
Bypassed specific inter-species interactions, instead modeling transitions between physiological states ('community states').
Analyzed the model to identify emergent properties of dynamical microbial communities.

Main Results:

Identified increased tolerance of community diversity to fast-growing species in different community states.
Observed enhanced community stability through staggered species dominance across different community states.
Found an increased requirement for growth dominance for the inclusion of late-growing species.

Conclusions:

Dynamical microbial communities exhibit distinct features like higher diversity tolerance and enhanced stability compared to steady-state communities.
The Community State model provides principles for understanding complex microbial dynamics.
Shifted focus from bottom-up interaction studies to top-down analysis of macroscopic observables like growth rates and biomass density for quantitative ecosystem examination.