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,...

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,...

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...

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...

Combinatorial Gene Control

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...

Constitutive and Regulated Gene Expression

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

Dietary patterns are not associated with overweight and obesity in a sample of 8900 Chinese preschool children from four cities.

Journal of nutritional science·2018

Same author

Synthesis of near-infrared fluorescent rhodamines via an S<sub>N</sub>Ar<sup>H</sup> reaction and their biological applications.

Organic & biomolecular chemistry·2018

Same author

Exome-wide analysis identifies three low-frequency missense variants associated with pancreatic cancer risk in Chinese populations.

Nature communications·2018

Same author

Inhibition of kinesin family member 20B sensitizes hepatocellular carcinoma cell to microtubule-targeting agents by blocking cytokinesis.

Cancer science·2018

Same author

Specific Salt Effect on the Interaction between Rare Earth Ions and Trioctylphosphine Oxide Molecules at the Organic-Aqueous Two-Phase Interface: Experiments and Molecular Dynamics Simulations.

Langmuir : the ACS journal of surfaces and colloids·2018

Same author

Low-repetition-rate all-fiber integrated optical parametric oscillator for coherent anti-Stokes Raman spectroscopy.

Optics express·2018

Same journal

Rapid Evolution of Expression Levels in Hepatocellular Carcinoma.

International journal of computational biology and drug design·2021

Same journal

Identifying the dynamic gene regulatory network during latent HIV-1 reactivation using high-dimensional ordinary differential equations.

International journal of computational biology and drug design·2021

Same journal

PATH: An interactive web platform for analysis of time-course high-dimensional genomic data.

International journal of computational biology and drug design·2021

Same journal

Modelling of hypoxia gene expression for three different cancer cell lines.

International journal of computational biology and drug design·2020

Same journal

Brain-wide structural connectivity alterations under the control of Alzheimer risk genes.

International journal of computational biology and drug design·2020

Same journal

Native State of Complement Protein C3d Analysed via Hydrogen Exchange and Conformational Sampling.

International journal of computational biology and drug design·2019

See all related articles

Search research articles

Related Experiment Video

Updated: May 14, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Identify condition-specific gene co-expression networks.

Vikram Kalluru¹, Raghu Machiraju, Kun Huang

¹Department of Electrical Engineering, The Ohio State University, Columbus, OH 43210, USA. kalluru.1@osu.edu

International Journal of Computational Biology and Drug Design

|February 23, 2013

Summary

This summary is machine-generated.

This study introduces a new method to identify condition-specific gene co-expression networks. This approach helps understand gene interaction dynamics in diseases like breast cancer.

More Related Videos

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Related Experiment Videos

Last Updated: May 14, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Gene co-expression patterns can differ across biological conditions, such as normal versus diseased states.
These differences are often driven by variations in transcription factor activity, leading to distinct co-expression networks.
Understanding these condition-specific networks is crucial for deciphering disease mechanisms.

Purpose of the Study:

To develop and apply a novel computational method for identifying condition-specific co-expression networks.
To compare transcriptional programs between basal and non-basal breast cancer subtypes.
To provide insights into gene interaction dynamics and the impact of perturbations in cancer.

Main Methods:

Combines a network quasi-clique mining algorithm with the expected conditional F-statistic.
Applies the method to analyze gene expression data from different breast cancer subtypes.
Focuses on identifying condition-specific gene interactions and regulatory patterns.

Main Results:

Successfully identified condition-specific co-expression networks differentiating breast cancer subtypes.
Revealed distinct transcriptional programs between basal and non-basal breast cancers.
Demonstrated the utility of the method for dynamic network characterization.

Conclusions:

The proposed method offers a novel perspective on studying gene interaction dynamics in cancer.
It enables the assessment of transcription factor perturbation effects on gene networks.
This approach facilitates a dynamic characterization of gene interaction networks in various conditions.