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

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

You might also read

Related Articles

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

Sort by

Same author

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026

Same author

PARiS: Probabilistic Assignment and Repartitioning of isomiR Sequences: A data-driven method for denoising isomiR read count data.

bioRxiv : the preprint server for biology·2026

Same author

MYC and Epithelial to Mesenchymal Transition (EMT) Independently Predict Circadian Rhythm Disruption in Lung Adenocarcinoma.

bioRxiv : the preprint server for biology·2025

Same author

miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs.

Genome biology·2025

Same author

Reply.

Gastroenterology·2025

Same author

Modeling and NMR Data Elucidate the Structure of a G-Quadruplex-Ligand Interaction for a Pu22T-Cyclometalated Iridium(III) System.

The journal of physical chemistry. B·2024

Search research articles

Related Experiment Video

Updated: Aug 27, 2025

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments.

Helene R McMurray¹, Harry A Stern², Aslihan Ambeskovic³

¹Department of Biomedical Genetics, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA; Department of Pathology and Laboratory Medicine, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA.

|October 1, 2022

Summary

This summary is machine-generated.

This study introduces TopNet, a reliable method for inferring gene regulatory networks from gene perturbation experiments. It models gene interdependence by analyzing perturbed and measured gene expression data for network visualization and validation.

Keywords:

Bioinformatics Cancer Cell Biology Cell culture Gene Expression Genetics Molecular Biology Systems biology

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

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Related Experiment Videos

Last Updated: Aug 27, 2025

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

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

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Area of Science:

Systems Biology
Computational Biology
Genetics

Background:

Gene regulatory networks (GRNs) are crucial for understanding cellular processes.
Inferring GRNs from gene perturbation experiments offers a reliable method to investigate gene interdependence.

Purpose of the Study:

To present a protocol for network modeling using TopNet, specifically designed for gene perturbation experiments.
To demonstrate the summarization, visualization, and validation of inferred gene regulatory networks.

Main Methods:

Gene perturbation experiments and expression measurements.
Network modeling using the TopNet software.
Summarization, visualization, and optional genetic testing of network dependencies.

Main Results:

Successful inference and modeling of gene regulatory networks from perturbation data.
Demonstration of TopNet's capability to model data where nodes are both perturbed and measured.
Visualization and validation of identified gene dependencies.

Conclusions:

TopNet provides a robust framework for inferring gene regulatory networks from gene perturbation data.
The protocol facilitates a comprehensive analysis from experimental design to network validation.
TopNet is applicable to diverse datasets involving perturbed and measured nodes.