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 Experiment Videos

Duplication-divergence model of protein interaction network.

I Ispolatov1, P L Krapivsky, A Yuryev

  • 1Ariadne Genomics Inc., Rockville, Maryland 20850, USA. iispolat@lauca.usach.cl

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 11, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Finite-time blowup of a Brownian particle in a repulsive potential.

Physical review. E·2025
Same author

Self-Reinforcing Cascades: A Spreading Model for Beliefs or Products of Varying Intensity or Quality.

Physical review letters·2025
Same author

Expansion into the vacuum of stochastic gases with long-range interactions.

Physical review. E·2025
Same author

Templating aggregation.

Physical review. E·2025
Same author

Universal dynamics of a passive particle driven by Brownian motion.

Physical review. E·2025
Same author

Current fluctuations in the Dyson gas.

Physical review. E·2025

This study models protein-protein interaction network evolution. A key parameter determines if network growth is self-averaging, impacting average node degree and degree distribution, aligning with real biological networks.

Area of Science:

  • Systems Biology
  • Network Science
  • Computational Biology

Background:

  • Protein-protein interaction (PPI) networks are crucial for cellular functions.
  • Understanding the evolutionary dynamics of PPI networks is essential for deciphering biological complexity.

Purpose of the Study:

  • To investigate a simplified model of PPI network evolution through duplication and divergence.
  • To analyze how a single parameter influences network growth and structure.

Main Methods:

  • Development of a simple mathematical model for network evolution.
  • Analysis of network behavior based on the probability of retaining duplicated links.
  • Comparison of model-predicted degree distributions with empirical data from real PPI networks.

Related Experiment Videos

Main Results:

  • The model exhibits distinct behaviors based on the link retention probability.
  • High link retention leads to non-self-averaging growth and algebraic increase in average node degree.
  • Low link retention ( < 50%) results in self-averaging growth, slow or constant average degree increase, and power-law degree distributions.
  • The model accurately predicts degree distributions found in real protein networks.

Conclusions:

  • A simple duplication-divergence model can recapitulate key features of PPI network evolution.
  • The probability of link retention is a critical factor governing network growth and topological properties.
  • The model provides a valuable framework for understanding the principles underlying biological network organization.