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

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...

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 Occurs at Multiple Steps

Regulation of Expression Occurs at Multiple Steps

You might also read

Related Articles

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

Sort by

Same author

Targeting the Myocardial Microenvironment: Novel Antiviral Strategies and Therapeutic Perspectives for Coxsackievirus B-Induced Myocarditis.

Journal of the American Heart Association·2026

Same author

Promera: a unified model for biomolecular structure prediction, filtering, and design.

bioRxiv : the preprint server for biology·2026

Same author

Spatiotemporal cell type deconvolution leveraging tissue structure.

Research square·2026

Same author

SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins.

ArXiv·2026

Same author

CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.

Bioinformatics (Oxford, England)·2026

Same author

Dango: Predicting higher-order genetic interactions.

Cell systems·2026

Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026

Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026

Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 25, 2025

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

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

Published on: April 21, 2023

CLARIFY: cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics.

Mihir Bafna¹, Hechen Li¹, Xiuwei Zhang¹

¹School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, United States.

Bioinformatics (Oxford, England)

|June 30, 2023

Summary

This summary is machine-generated.

CLARIFY infers cell-cell interactions (CCIs) and gene regulatory networks (GRNs) simultaneously using spatial transcriptomic data. This novel approach improves the accuracy of both GRN and CCI inference compared to existing methods.

More Related Videos

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Related Experiment Videos

Last Updated: Jul 25, 2025

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

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

Published on: April 21, 2023

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Gene regulatory networks (GRNs) control cellular functions through feedback loops.
Cell-cell interactions (CCIs) also influence cellular behavior, especially in spatial contexts.
Existing methods often infer GRNs and CCIs in isolation, neglecting their interdependence.

Purpose of the Study:

To develop a unified computational model for co-inferring GRNs and CCIs.
To leverage spatial transcriptomic data for a more accurate biological network analysis.
To introduce a novel tool, CLARIFY, for integrated GRN and CCI inference.

Main Methods:

CLARIFY utilizes a multi-level graph autoencoder architecture.
The model integrates spatially resolved gene expression data.
It refines cell-specific GRNs while inferring CCIs.

Main Results:

CLARIFY demonstrated superior performance in inferring both GRNs and CCIs on real and simulated datasets.
Co-inference significantly improved accuracy compared to methods inferring GRNs or CCIs separately.
The tool was validated on seqFISH and MERFISH spatial transcriptomic data.

Conclusions:

Simultaneous inference of GRNs and CCIs is crucial for understanding cellular systems.
Layered graph neural networks are effective for analyzing complex biological networks.
CLARIFY provides a robust framework for integrated network inference from spatial data.