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

DNA Packaging00:58

DNA Packaging

113.5K
Overview
113.5K
Chromatin Packaging01:32

Chromatin Packaging

19.5K
Each human somatic cell contains 6 billion base pairs of DNA. Each base pair is 0.34 nm long, meaning each diploid cell contains a staggering 2 meters of DNA. This long DNA strand is packed inside a nucleus measuring only 10-20 microns in diameter with the help of specialized DNA-binding proteins called histones. Together they form a compact DNA-protein complex called chromatin. The chromatin is further compacted into higher-order structures. The highest level of compaction is achieved during...
19.5K
Chromatin Packaging02:21

Chromatin Packaging

22.3K
Each human somatic cell contains 6 billion base-pairs of DNA. Each base-pair is 0.34 nm long, which means that each diploid cell contains a staggering 2 meters of DNA. How is such a long DNA strand packed inside a nucleus measuring only 10 - 20 microns in diameter? 
The chromatin
In combination with specialized DNA binding protein called Histones, the DNA double helix forms a compact DNA: protein complex called chromatin. The chromatin itself is further compacted into higher-order...
22.3K
Chromatin Packaging02:21

Chromatin Packaging

9.9K
9.9K
Protein Networks02:26

Protein Networks

4.6K
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,...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K

You might also read

Related Articles

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

Sort by
Same author

Protein QID74 protects the cell wall of <i>Trichoderma</i> from degradation caused by its own chitinase, which lacks a carbohydrate-binding module.

mBio·2026
Same author

Nutritional value improvement of soybean meal through solid-state fermentation by proteases-enhanced Streptomyces sp. SCUT-3.

International journal of biological macromolecules·2025
Same author

Chitinases are important virulence factors in Vibrio for degrading the chitin-rich barrier of shrimp.

International journal of biological macromolecules·2024
Same author

Bioconversion of agriculture by-products with functionally enhanced Streptomyces sp. SCUT-3: Fish skin as a model.

Food chemistry·2024
Same author

Sustainable valorizing high-protein feather waste utilization through solid-state fermentation by keratinase-enhanced Streptomyces sp. SCUT-3 using a novel promoter.

Waste management (New York, N.Y.)·2023
Same author

Degradation of indole via a two-component indole oxygenase system from Enterococcus hirae GDIAS-5.

Journal of hazardous materials·2023
Same journal

Mosquito Species and Gender Identification System Based on Artificial Intelligence and Image Processing Methods.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

Related Experiment Video

Updated: Feb 12, 2026

Freezing, Thawing, and Packaging Cells for Transport
07:32

Freezing, Thawing, and Packaging Cells for Transport

Published on: July 2, 2008

13.1K

PyPathway: Python Package for Biological Network Analysis and Visualization.

Yang Xu1, Xiao-Chun Luo1

  • 1Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, School of Bioscience and Bioengineering, South China University of Technology , Guangzhou Higher Education Mega Center, Guangzhou, China .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 12, 2018
PubMed
Summary
This summary is machine-generated.

PyPathway is a new Python package for analyzing biological networks. It aids in understanding complex diseases and molecular relationships through network modeling and visualization.

Keywords:
enrichment analysisnetwork analysispathwayvisualization

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

2.7K
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

3.7K

Related Experiment Videos

Last Updated: Feb 12, 2026

Freezing, Thawing, and Packaging Cells for Transport
07:32

Freezing, Thawing, and Packaging Cells for Transport

Published on: July 2, 2008

13.1K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

2.7K
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

3.7K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Life science studies generate large datasets due to advances in sequencing technology.
  • Biological networks are crucial for interpreting high-throughput data and understanding disease complexity.
  • Existing Python tools lack comprehensive network analysis and visualization capabilities.

Purpose of the Study:

  • To develop PyPathway, an open-source Python package for functional enrichment analysis, network modeling, and visualization.
  • To provide a tool that supports various biological network and pathway databases.
  • To enable users to share their network analysis results via a web application.

Main Methods:

  • Developed PyPathway, an extensible Python package.
  • Integrated support for multiple interaction network and pathway databases (Reactome, WikiPathway, STRING, BioGRID).
  • Implemented network analysis methods including overrepresentation analysis, gene set enrichment analysis, and de novo network modeling.
  • Created visualization and data publishing modules for easy sharing of analysis.

Main Results:

  • PyPathway offers a comprehensive suite of tools for biological network analysis and visualization.
  • The package supports diverse biological databases and advanced analytical methods.
  • Users can easily share their findings through an integrated web application.

Conclusions:

  • PyPathway addresses the need for advanced network analysis and visualization tools in life sciences.
  • The package facilitates a deeper understanding of molecular complexity and relationships in diseases.
  • PyPathway is a valuable, free, and open-source resource for the scientific community.