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...
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...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
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...

You might also read

Related Articles

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

Sort by
Same author

Training that sticks: sustaining knowledge and communication excellence in geriatric oncology through a multidisciplinary training program.

Innovation in aging·2026
Same author

Pembrolizumab, Temozolomide and HSPPC-96 Vaccine in Newly Diagnosed Glioblastoma Post-Chemoradiation: Results from a Multi-institutional, Phase 2, Randomized, Placebo-Controlled Trial.

medRxiv : the preprint server for health sciences·2026
Same author

Continuous glucose monitoring to support the diagnosis of MODY: A multicenter cross-sectional and prospective study.

Medicina clinica·2026
Same author

Technological Integration in Aesthetic Practice: A Systematic Review of Artificial Intelligence, Augmented Reality and Robotics in Cosmetic Procedures.

Aesthetic plastic surgery·2026
Same author

Major limb replantation: Current status and perspectives.

JPRAS open·2026
Same author

The challenge of one billion adjuvanted vaccine doses: evaluating scalability, sustainability, and supply capacity of Quillaja saponin QS-21 for large-scale vaccine demand.

Frontiers in immunology·2026

Related Experiment Video

Updated: May 10, 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

Inferring latent gene regulatory network kinetics.

Javier González1, Ivan Vujačić, Ernst Wit

  • 1Mathematics, Statistics and Probability Unit, University of Groningen, Groningen, Groningen 9747 AG, The Netherlands. j.gonzalez.hernandez@rug.nl

Statistical Applications in Genetics and Molecular Biology
|June 8, 2013
PubMed
Summary

This study introduces a new statistical framework to infer gene regulatory network parameters and transcription factor (TF) activity from gene expression data. The method is computationally efficient and accurately models TF behavior in biological systems.

More Related Videos

Measuring the Kinetics of mRNA Transcription in Single Living Cells
11:22

Measuring the Kinetics of mRNA Transcription in Single Living Cells

Published on: August 25, 2011

An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations
11:36

An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

Related Experiment Videos

Last Updated: May 10, 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

Measuring the Kinetics of mRNA Transcription in Single Living Cells
11:22

Measuring the Kinetics of mRNA Transcription in Single Living Cells

Published on: August 25, 2011

An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations
11:36

An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Gene regulatory networks control cellular processes through transcription factors (TFs) and their target genes.
  • Modeling these networks often uses ordinary differential equations (ODEs), but TF abundance is difficult to measure directly.
  • Estimating kinetic parameters and TF activity from gene expression data presents a significant challenge.

Purpose of the Study:

  • To develop a general statistical framework for inferring kinetic parameters of regulatory networks with unobserved transcription factors.
  • To predict the activity levels of transcription factors using time-course gene expression data.
  • To provide a computationally efficient method suitable for large-scale biological systems.

Main Methods:

  • A penalized likelihood approach is proposed, utilizing ODEs as a penalty term.
  • The method infers kinetic parameters and TF activity without explicitly solving the ODEs.
  • Applied to time-course gene expression data for regulatory network analysis.

Main Results:

  • The framework successfully infers kinetic parameters and predicts TF activity profiles.
  • The approach demonstrates computational efficiency, making it suitable for complex networks.
  • Validation on the SOS repair system in Escherichia coli showed accurate TF behavior reconstruction.

Conclusions:

  • The proposed statistical framework offers an efficient and accurate method for modeling gene regulatory networks with unobserved transcription factors.
  • This approach advances the understanding of gene regulation by enabling robust inference from gene expression data.
  • The method has broad applicability in systems biology for analyzing complex genetic circuits.