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

Genetic Screens02:46

Genetic Screens

5.9K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.9K
Protein Networks02:26

Protein Networks

4.7K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.7K
Protein Networks02:26

Protein Networks

3.0K
3.0K
Combinatorial Gene Control02:33

Combinatorial Gene Control

10.4K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
10.4K
DNA Microarrays02:34

DNA Microarrays

23.3K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
23.3K

You might also read

Related Articles

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

Sort by
Same author

Mucin-derived sugars act as metabolic brakes controlling growth initiation in <i>Akkermansia muciniphila</i>.

Gut microbes·2026
Same author

First-In-Human Trial of Encapsulated Cells Constitutively Expressing Localized IL-2 in Patients with High-Grade Serous Ovarian Carcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Unraveling discrimination strategies in biological error-correction networks.

The Journal of chemical physics·2026
Same author

Biophysical Modeling Elucidates Mechanistic Principles for Rational Molecular Glue Design.

Journal of chemical information and modeling·2026
Same author

Deep learning framework for quantifying self-organization in <i>Myxococcus xanthus</i>.

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

Collective RNAP Dynamics Link Transcriptional Strength to Fidelity.

The journal of physical chemistry letters·2026
Same journal

The role of the antimicrobial peptide nisin as a clean label food preservative.

Current opinion in microbiology·2026
Same journal

From coarse-grained metabolic rules to fine-grained control of microbial communities.

Current opinion in microbiology·2026
Same journal

Progress in engineered bacterial cancer therapies.

Current opinion in microbiology·2026
Same journal

Constraints on adaptive loss-of-function mutations during microbial metabolic interactions.

Current opinion in microbiology·2026
Same journal

Discovery of novel antimicrobials within microbiomes.

Current opinion in microbiology·2026
Same journal

Beyond the protein lattice: bacterial S-layer glycans - from structure to functional frontier.

Current opinion in microbiology·2026
See all related articles

Related Experiment Video

Updated: Apr 17, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K

How to train your microbe: methods for dynamically characterizing gene networks.

Sebastian M Castillo-Hair1, Oleg A Igoshin2, Jeffrey J Tabor3

  • 1Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77005, United States.

Current Opinion in Microbiology
|February 14, 2015
PubMed
Summary
This summary is machine-generated.

Researchers are exploring dynamic gene perturbations to understand how gene networks function. This approach offers new insights into biological organization beyond static methods, revealing DNA to phenotype regulation.

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.7K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

9.4K

Related Experiment Videos

Last Updated: Apr 17, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.7K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

9.4K

Area of Science:

  • Systems Biology
  • Molecular Biology
  • Genetics

Background:

  • Gene networks dynamically regulate biological processes.
  • Traditional methods like static perturbations (gene knockouts, media variations) limit understanding of gene network dynamics.
  • Much regulation from DNA to phenotype remains poorly understood due to reliance on static approaches.

Purpose of the Study:

  • To introduce major classes of dynamical perturbations for studying gene networks.
  • To discuss technologies for creating temporal perturbations in microbial pathways.
  • To advance understanding of gene network structure and function using dynamic approaches.

Main Methods:

  • Utilizing improved genetic tools, hardware, and computational control strategies.
  • Generating precise temporal perturbations both outside and inside live cells.
  • Applying these methods across a wide range of microbial pathways.

Main Results:

  • Recent studies using temporal perturbations have provided new insights into biological organizing principles.
  • Dynamical perturbations offer a more comprehensive view of gene network regulation.
  • This approach enhances the understanding of the path from DNA to phenotype.

Conclusions:

  • Dynamical perturbations are crucial for fully elucidating gene network function.
  • Technological advancements enable precise temporal control for biological research.
  • This work highlights the potential of dynamic perturbations in microbial systems biology.