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

1.6K
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...
1.6K
Master Transcription Regulators02:23

Master Transcription Regulators

8.0K
Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
8.0K
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

27.5K
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...
27.5K
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

4.4K
4.4K

You might also read

Related Articles

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

Sort by
Same author

Methanol Clipping Modification on Liquid Metal Surface Enhances Photothermal Performance and Biocompatibility.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

IEEE transactions on visualization and computer graphics·2026
Same author

Total-Body Dynamic PET/CT Imaging of Proton-Induced Activity and Biologic Washout After Proton Therapy.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same author

Unlocking the Quality Potential of Liberoid Coffee: Advances in Composition, Processing, and Microbial Fermentation.

Comprehensive reviews in food science and food safety·2026
Same author

Prognostic value of the triglyceride-glucose (TyG) index for renal function progression in patients with CKD stages 3-4.

Frontiers in nutrition·2026
Same author

Non-Arrhenius threshold switching by field-driven dipolar ordering.

Nature communications·2026

Related Experiment Video

Updated: Apr 15, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
12:44

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

12.8K

Regulatory network inferred using expression data of small sample size: application and validation in erythroid

Fan Zhu1, Lihong Shi2, James Douglas Engel3

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.

Bioinformatics (Oxford, England)
|April 4, 2015
PubMed
Summary

This study introduces a novel multi-layer graphical model to accurately map gene regulatory networks during cell differentiation. The model integrates multiple datasets, revealing novel erythropoiesis-specific interactions validated by experimental data.

More Related Videos

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
09:07

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Published on: June 21, 2016

8.8K
Mouse Fetal Liver Culture System to Dissect Target Gene Functions at the Early and Late Stages of Terminal Erythropoiesis
06:40

Mouse Fetal Liver Culture System to Dissect Target Gene Functions at the Early and Late Stages of Terminal Erythropoiesis

Published on: September 9, 2014

16.0K

Related Experiment Videos

Last Updated: Apr 15, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
12:44

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

12.8K
Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
09:07

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Published on: June 21, 2016

8.8K
Mouse Fetal Liver Culture System to Dissect Target Gene Functions at the Early and Late Stages of Terminal Erythropoiesis
06:40

Mouse Fetal Liver Culture System to Dissect Target Gene Functions at the Early and Late Stages of Terminal Erythropoiesis

Published on: September 9, 2014

16.0K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Gene regulatory network modeling is crucial for understanding cell differentiation.
  • Existing methods struggle with limited sample sizes and context-specific interactions.

Purpose of the Study:

  • To develop a robust method for modeling context-specific regulatory networks during differentiation.
  • To leverage multiple public datasets and small cell lineage-specific data for improved accuracy.

Main Methods:

  • A multi-layer graphical model was developed.
  • Network inference methods were applied to individual datasets.
  • Inferred relationships were weighted and integrated using cell lineage-specific data.

Main Results:

  • The model successfully inferred erythropoiesis-specific regulatory networks.
  • Novel regulatory relationships were identified and experimentally validated.
  • These findings were not achievable with single-dataset or context-independent approaches.

Conclusions:

  • The developed model accurately captures context-specific regulatory dynamics in differentiation.
  • This approach enhances the understanding of lineage commitment and cellular identity.