Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Environmental Applications of Microorganisms01:30

Environmental Applications of Microorganisms

430
Microorganisms play a pivotal role in maintaining ecosystem balance by recycling essential elements such as carbon, nitrogen, and phosphorus, as well as supporting processes like bioremediation, wastewater treatment, and biofuel production.Microbes in Elemental CyclesIn the carbon cycle, microorganisms decompose organic matter, releasing carbon dioxide via aerobic respiration. This carbon dioxide is subsequently used by photosynthetic organisms to synthesize organic compounds, closing the...
430
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

191
Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
191
Gene Regulation in Microbial Communities: Quorum Sensing01:28

Gene Regulation in Microbial Communities: Quorum Sensing

132
Quorum sensing is a mechanism of bacterial communication that enables coordinated gene expression in response to changes in population density. This facilitates collective behaviors that enhance survival, resource acquisition, and ecological adaptation. This process relies on small signaling molecules called autoinducers that accumulate as bacterial populations grow. When a critical threshold concentration of autoinducers is reached, bacterial cells collectively modify gene expression,...
132

You might also read

Related Articles

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

Sort by
Same author

Modelling the role of the microbiome in antimicrobial resistance across scales.

Nature microbiology·2026
Same author

Deconfounded, quantitative microbiome profiling identifies robust multiple sclerosis markers and clinical covariate associations.

Gut microbes·2026
Same author

Large-scale analysis of temporal gene expression variation in peripheral blood.

Nature communications·2026
Same author

Exposure to currently used pesticides in Belgian children: Urinary biomonitoring and risk assessment of frequently detected chlorpyrifos and pyrethroid metabolites.

Environmental research·2026
Same author

Kidney Function Modulates Gut Microbial Metabolism.

Toxins·2026
Same author

Toward ethical human microbiome research: improving health through radical interdisciplinary and intercultural co-laboration.

Microbiome·2026

Related Experiment Video

Updated: Oct 12, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.5K

Disentangling environmental effects in microbial association networks.

Ina Maria Deutschmann1, Gipsi Lima-Mendez2, Anders K Krabberød3

  • 1Institute of Marine Sciences, CSIC, Passeig Marítim de la Barceloneta, 37-49, 08003, Barcelona, Spain. ina.m.deutschmann@gmail.com.

Microbiome
|November 26, 2021
PubMed
Summary

Distinguishing environmentally driven associations is key for understanding microbial ecological interactions. The EnDED method helps identify and remove these non-interactive links in microbial networks.

Keywords:
Association networkEffect of indirect dependenciesEnvironmentally driven edge detectionMicrobial interactions

More Related Videos

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

46.6K
Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans
07:19

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

Published on: September 13, 2022

2.4K

Related Experiment Videos

Last Updated: Oct 12, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.5K
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

46.6K
Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans
07:19

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

Published on: September 13, 2022

2.4K

Area of Science:

  • Microbiology
  • Ecology
  • Bioinformatics

Background:

  • Microbial interactions are crucial for ecosystem function but poorly understood.
  • High-throughput omics data reveal microbial associations as networks.
  • Distinguishing ecological interactions from environmental selection in these networks is essential.

Purpose of the Study:

  • To develop and evaluate a method for identifying environmentally driven associations in microbial networks.
  • To differentiate true ecological interactions from associations driven by environmental factors.

Main Methods:

  • EnDED (environmentally driven edge detection) was implemented using four approaches: sign pattern, overlap, interaction information, and data processing inequality.
  • Methods were tested individually and in combination on simulated microbial networks.
  • The approach was applied to a real-world marine microbial association network.

Main Results:

  • Individual EnDED methods detected 44%-87% of environmentally driven edges in simulated data.
  • A combined intersection approach identified 32% of environmentally driven associations.
  • In a marine microbial network, the intersection approach identified 8.3% environmentally driven associations, compared to 24.8%-84.6% for individual methods.

Conclusions:

  • EnDED provides methods to identify and quantify environmentally driven associations in microbial networks.
  • Removing environmentally driven associations is critical for accurate ecological interaction hypotheses.
  • Combining EnDED methods with other strategies is recommended to reduce false associations.