Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cell Specific Gene Expression

Cell Specific Gene Expression

Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

Regulation of Expression at Multiple Steps

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...

Regulation of Expression Occurs at Multiple Steps

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 Steps

Regulation of Expression Occurs at Multiple Steps

Interactions Between Signaling Pathways

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...

You might also read

Related Articles

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

Sort by

Same author

Mg(OH)<sub>2</sub> Nanoflakes for Effective Removal of Phosphate Ions.

ChemistryOpen·2026

Same author

The pyruvate branch point controls lymphoid cancer cell dissemination.

bioRxiv : the preprint server for biology·2026

Same author

Thioredoxin interacting protein (TXNIP), a redox regulator, mediates the RAPGEF3/4 signaling dependency in primary melanoma.

bioRxiv : the preprint server for biology·2026

Same author

A network-based model of Aspergillus fumigatus elucidates regulators of development and defensive natural products of an opportunistic pathogen.

Nucleic acids research·2026

Same author

Defining the role of β-cell IRE1α/XBP1 pathway and its gene regulatory network components in non-obese diabetic mice.

Nature communications·2025

Same author

Corrigendum to "Composite inclusion complexes containing hyaluronic acid/chitosan nanosystems for dual responsive enrofloxacin release" [Carbohydrate Polymers 252 (2021) 117162].

Carbohydrate polymers·2025

Same journal

Common Principles Underlie Mitochondrial DNA Heteroplasmy Dynamics in the Germline and Soma.

Annual review of genomics and human genetics·2026

Same journal

Human Sex Chromosome Biology in the Genomic Era.

Annual review of genomics and human genetics·2026

Same journal

Beyond TADs and Compartments: Mesoscale Chromatin Folding and Its Dynamics in Transcriptional Regulation.

Annual review of genomics and human genetics·2026

Same journal

Experimental and Computational Approaches to Identify Noncoding Pathogenic Variation in Rare Disease.

Annual review of genomics and human genetics·2026

Same journal

How Studying Rare Disease Leads to Mechanistic Insights and Therapeutic Development: Lessons from Nonmammalian Models.

Annual review of genomics and human genetics·2026

Same journal

Synthetic Regulatory Genomics.

Annual review of genomics and human genetics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 23, 2026

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Published on: March 22, 2018

Modeling Dynamics, Cell Type Specificity, and Perturbations in Gene Regulatory Networks.

Junha Shin¹, Spencer Halberg-Spencer^1,2, Yuda Liu^1,2

¹1Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, USA;

Annual Review of Genomics and Human Genetics

|April 21, 2026

Summary

This summary is machine-generated.

This review explores inferring gene regulatory networks (GRNs) from single-cell omics data. It highlights advances in understanding cell-specific mechanisms and dynamics, while noting open challenges.

More Related Videos

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

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

Published on: December 7, 2021

A Microfluidics Approach for the Functional Investigation of Signaling Oscillations Governing Somitogenesis

A Microfluidics Approach for the Functional Investigation of Signaling Oscillations Governing Somitogenesis

Published on: March 19, 2021

Related Experiment Videos

Last Updated: Apr 23, 2026

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Published on: March 22, 2018

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

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

Published on: December 7, 2021

A Microfluidics Approach for the Functional Investigation of Signaling Oscillations Governing Somitogenesis

A Microfluidics Approach for the Functional Investigation of Signaling Oscillations Governing Somitogenesis

Published on: March 19, 2021

Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Gene regulatory networks (GRNs) govern essential biological processes like development and disease.
Characterizing GRNs across diverse cell types and states is a significant challenge.
Single-cell omics technologies offer high-resolution biological system measurements.

Purpose of the Study:

To review current methods for inferring GRNs from single-cell omic datasets.
To focus on the inference of dynamic regulatory processes and responses to perturbations.
To identify key challenges and future directions in the field.

Main Methods:

Leveraging single-cell omics data (e.g., scRNA-seq).
Applying computational inference algorithms.
Analyzing dynamic and perturbation-based datasets.

Main Results:

Single-cell omics enables unprecedented resolution for GRN inference.
Computational methods provide insights into cell type-specific mechanisms and causality.
Focus on dynamics and perturbations reveals complex regulatory behaviors.

Conclusions:

Advances in single-cell omics and computational methods are transforming GRN inference.
Further research is needed to address challenges in inferring dynamic and perturbed GRNs.
Accurate GRN inference is crucial for understanding complex biological systems.