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

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...
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...

You might also read

Related Articles

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

Sort by
Same author

Microbial-geochemical Interactions in Underground Reservoirs: Implications for Hydrogen Storage.

Microbial ecology·2026
Same author

Predicting coarse-grained representations of biogeochemical cycles from metabarcoding data.

Bioinformatics (Oxford, England)·2025
Same author

Is Cancer Metabolism an Atavism?

Cancers·2024
Same author

Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains.

eLife·2023
Same author

Dynamical modeling of the H3K27 epigenetic landscape in mouse embryonic stem cells.

PLoS computational biology·2022
Same author

Multiomics Study of Bacterial Growth Arrest in a Synthetic Biology Application.

ACS synthetic biology·2021
Same journal

The bridges evolution built: In search of mechanisms that couple scales of perception and action.

Bio Systems·2026
Same journal

Spatiotemporal bursting in simulated cultures of cortical neurons.

Bio Systems·2026
Same journal

A brief discussion on recent models shedding light on how life emerged.

Bio Systems·2026
Same journal

Memory-based strategy reputation and adaptive learning in spatial evolutionary games: A robust agent-based model for cooperation dynamics.

Bio Systems·2026
Same journal

Coherent Photonic Biofields: Revisiting Fritz-Albert Popp's Hypothesis.

Bio Systems·2026
Same journal

Ruliological Resilience: Pattern Restoration and Robustness in Wolfram Patterns. A Basis for Regeneration, Not Just in Cone Shells?

Bio Systems·2026
See all related articles

Related Experiment Video

Updated: Jun 21, 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

A declarative constraint-based method for analyzing discrete genetic regulatory networks.

Fabien Corblin1, Sébastien Tripodi, Eric Fanchon

  • 1Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité-Informatique, Mathématiques et Applications de Grenoble, Université Joseph Fourier CNRS-UMR 5525, Domaine de la Merci, La Tronche, France. Fabien.Corblin@imag.fr

Bio Systems
|August 12, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a declarative method for building and analyzing discrete models of genetic regulatory networks (GRNs), overcoming uncertainties in network structure and parameters. The approach uses constraints to systematically construct and refine GRN models, improving biological understanding.

More Related Videos

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
10:46

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

Related Experiment Videos

Last Updated: Jun 21, 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

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
10:46

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Dynamical modeling aids understanding of genetic regulatory networks (GRNs).
  • Model development faces challenges due to uncertainties in network structure and parameters.
  • Current iterative, generate-and-test approaches are simulation-intensive.

Purpose of the Study:

  • To present a novel, systematic four-step method for constructing and analyzing discrete GRN models.
  • To offer an alternative to traditional generate-and-test modeling approaches.
  • To enhance the identification of crucial experiments for refining GRN models.

Main Methods:

  • A declarative approach formulating biological knowledge as constraints.
  • Querying network structure compatibility with constraints and relaxing inconsistencies.
  • Analyzing common properties of consistent models using dedicated languages.
  • Applying the method to model the nutritional stress response in Escherichia coli.

Main Results:

  • Demonstrated feasibility of the declarative approach for GRN modeling.
  • Systematic construction and analysis of discrete GRN models.
  • Identification of potential experiments to refine model understanding.

Conclusions:

  • The declarative method offers a systematic alternative to traditional GRN modeling.
  • This approach reduces reliance on extensive simulations and parameter fitting.
  • It facilitates targeted experimental design for improved biological insight.