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

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Protein Networks02:26

Protein Networks

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,...
Combinatorial Gene Control02:33

Combinatorial Gene Control

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...

You might also read

Related Articles

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

Sort by
Same author

Re-shaping the family-level classification of <i>Agaricineae</i> (<i>Agaricales</i>, <i>Basidiomycota</i>) using a phylogenomic approach.

Studies in mycology·2026
Same author

Phase-Only Rotation Invariant Correlation Using Synthesized Phase Objects.

Journal of nanoscience and nanotechnology·2018
Same author

Effect of prolonged inspiratory time on gas exchange during robot-assisted laparoscopic urologic surgery.

Der Anaesthesist·2018
Same author

Safety of hydroxyethyl starch 130/0.4 in paediatric cardiac patients - a reply.

Anaesthesia·2018
Same author

Acute Basilar Artery Occlusion: Differences in Characteristics and Outcomes after Endovascular Therapy between Patients with and without Underlying Severe Atherosclerotic Stenosis.

AJNR. American journal of neuroradiology·2017
Same author

Deformation mechanisms to ameliorate the mechanical properties of novel TRIP/TWIP Co-Cr-Mo-(Cu) ultrafine eutectic alloys.

Scientific reports·2017
Same journal

Identification of MTFR1 as a Novel Prognostic Biomarker and Putative Oncogene for Breast Cancer: A Multi-Omics Analysis and in Vitro Experimental Validation.

IET systems biology·2026
Same journal

scGMB: A scRNA-seq Cell Classification Method Combining GCN and Mamba.

IET systems biology·2026
Same journal

Identification of Chemokine-Related Genes Derived From T and NK Cells in the Tumour Microenvironment of Ovarian Cancer Based on scRNA-Seq.

IET systems biology·2026
Same journal

Unravelling the Mechanism of Compound Kushen Injection in Treating Cervical Cancer Through Ferroptosis Regulation: An Integrated Network Pharmacology and Molecular Docking Study.

IET systems biology·2026
Same journal

Metabolic Reprogramming in Recurrent Spontaneous Abortion: Key Biomarkers Identification and Diagnostic Model Development.

IET systems biology·2026
Same journal

Network Pharmacology and Experimental Validation to Explore the Potential Mechanism of Salvianolic Acid B in Reversing Oxaliplatin Resistance of Colorectal Cancer Cells.

IET systems biology·2026
See all related articles

Related Experiment Video

Updated: May 24, 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

DoGeNetS: using optimisation to discriminate regulatory network topologies based on gene expression data.

A V Camargo-Rodriguez1, J T Kim

  • 1Aberystwyth University, IBERS, Aberystwyth, UK. avc1@aber.ac.uk

IET Systems Biology
|February 25, 2012
PubMed
Summary
This summary is machine-generated.

DoGeNetS directly assesses gene regulatory network (GRN) models using gene expression data. This method effectively discriminates between GRN structures, even with noisy data, aiding in understanding gene regulation dynamics.

More Related Videos

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Related Experiment Videos

Last Updated: May 24, 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

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Gene regulatory networks (GRNs) govern gene expression dynamics, but inferring their structure from data is challenging.
  • Current methods often rely on integrating diverse information, yet direct assessment of candidate models against expression data is needed.

Purpose of the Study:

  • To introduce DoGeNetS (Discrimination of Gene Network Structures), a novel method for directly assessing and discriminating candidate GRN models.
  • To evaluate the method's ability to correlate structural divergence with gene expression data divergence.

Main Methods:

  • DoGeNetS models GRN structures using the transsys language and optimizes numerical parameters to fit target gene expression data.
  • Multiple optimization runs generate score sets for statistically discriminating candidate models based on their explanatory potential.
  • The method's discrimination power is demonstrated by comparing structural divergence with expression profile divergence using known models and simulated data.

Main Results:

  • Structural divergence of GRN models strongly correlates with the divergence of their corresponding gene expression profiles after optimization.
  • DoGeNetS successfully discriminates between candidate GRN structures, even at noise levels exceeding typical microarray data.
  • The method can identify the best GRN structure from a small set of candidates, with p-values indicating statistical significance.

Conclusions:

  • DoGeNetS provides a robust approach for evaluating and discriminating between competing GRN models using gene expression data.
  • The method is adaptable to various experimental conditions, including simulating a wide range of perturbations beyond single gene knockouts.
  • DoGeNetS enhances the understanding of gene regulatory mechanisms by enabling direct comparison of network structures.