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

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

3.4K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
3.4K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

773
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
773
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.5K
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...
8.5K
Coordination of Gene Expression Processes in Bacteria01:29

Coordination of Gene Expression Processes in Bacteria

197
The DNA replication, transcription, and translation processes are intricately coupled in bacteria, allowing efficient gene expression and rapid protein synthesis. While this physical and functional coordination is advantageous, it introduces challenges that bacteria overcome through specific regulatory mechanisms.Coupling of Replication, Transcription, and TranslationThe coupling of replication, transcription, and translation is a hallmark of bacterial gene expression. As the replisome unwinds...
197
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

1.1K
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...
1.1K
What is Gene Expression?01:42

What is Gene Expression?

172.8K
Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
172.8K

You might also read

Related Articles

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

Sort by
Same author

The Single-Cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors.

Cell genomics·2026
Same author

An AI-Powered Trisomy 21 Research Assistant.

bioRxiv : the preprint server for biology·2026
Same author

Beyond Identifier Matching: An Empirical Characterization of Failure Modes in Biomedical Knowledge Graph Integration.

medRxiv : the preprint server for health sciences·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

Advances in Protein Function Prediction from the Fifth CAFA Challenge.

bioRxiv : the preprint server for biology·2026
Same author

Transcriptomic subtypes in high-grade serous ovarian cancer are driven by tumor cellular composition.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Sep 27, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.5K

Parameterized algorithms for identifying gene co-expression modules via weighted clique decomposition.

Madison Cooley1, Casey S Greene2, Davis Issac3

  • 1University of Utah.

Proceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms. SIAM Conference on Applied and Computational Discrete Algorithms (2021 : Online)
|April 8, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational model to find gene regulatory modules in gene co-expression data. The model uses weighted clique decomposition and is computationally efficient for identifying these biological networks.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K

Related Experiment Videos

Last Updated: Sep 27, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.5K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Gene co-expression data analysis is crucial for understanding gene regulation.
  • Identifying regulatory modules is challenging due to complex interaction effects.
  • Existing methods may not fully capture the nuances of gene interactions.

Purpose of the Study:

  • To develop a novel combinatorial model for identifying regulatory modules in gene co-expression data.
  • To generalize the weighted edge clique partition problem for capturing complex gene interactions.
  • To establish the computational tractability of the proposed model.

Main Methods:

  • The study presents a decomposition into weighted cliques to model regulatory modules.
  • It generalizes the weighted edge clique partition problem.
  • The research focuses on a noise-free setting and demonstrates fixed-parameter tractability.
  • Two new algorithms utilizing linear programming and integer partitioning are introduced for clique weight determination.

Main Results:

  • The proposed model is computationally tractable when parameterized by the number of modules.
  • Two novel algorithms were developed and implemented in Python.
  • The algorithms were tested on synthetic data generated from real-world transcription factor data and latent variable analysis.

Conclusions:

  • The new combinatorial model provides an effective approach for identifying regulatory modules.
  • The developed algorithms offer efficient solutions for analyzing gene co-expression data.
  • This work contributes to a better understanding of gene regulatory networks through computational methods.