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

Network motif identification in stochastic networks.

Rui Jiang1, Zhidong Tu, Ting Chen

  • 1Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA 90089, USA.

Proceedings of the National Academy of Sciences of the United States of America
|June 14, 2006
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

Metagenomic Hi‑C Protocols for Viral Genome Binning, Taxonomic Annotation, and Interaction Network Visualization.

Current protocols·2026
Same author

Benchmarking alignment strategies for Hi-C reads in metagenomic Hi-C data.

Genome biology·2026
Same author

A Beginner's Guide to Using DeepVirFinder for Viral Sequence Identification From Metagenomic Datasets.

Current protocols·2026
Same author

Correction: Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations.

PLoS computational biology·2026
Same author

ViTrace detects viral signatures in tumor transcriptomes using a hybrid language model.

Communications biology·2025
Same author

Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations.

PLoS computational biology·2025

This study introduces stochastic network motifs, the fundamental units of complex networks with inherent uncertainties. The developed method identifies these motifs in biological networks, revealing key patterns in gene regulation and protein interactions.

Area of Science:

  • Systems biology
  • Network science
  • Computational biology

Background:

  • Network motifs are recognized as fundamental building blocks in complex networks across various scientific domains.
  • Many real-world networks exhibit inherent uncertainties, necessitating their treatment as stochastic systems.
  • The building blocks within these stochastic networks may also possess probabilistic characteristics.

Purpose of the Study:

  • To investigate stochastic network motifs, which are derived from families of similar yet not identical interconnection patterns.
  • To develop a computational framework for identifying these stochastic motifs in complex networks.

Main Methods:

  • Establishment of a finite mixture model tailored for stochastic networks.
  • Development of an expectation-maximization algorithm specifically designed for identifying stochastic network motifs.

Related Experiment Videos

  • Application of the developed approach to biological networks, including transcriptional regulatory networks and protein-protein interaction networks.

    • Main Results:

    • Identification of several novel stochastic network motifs.
    • Validation of identified motifs against existing biological knowledge in model organisms.
    • Demonstration of the method's applicability to diverse biological network types.

    Conclusions:

    • Stochastic network motifs represent crucial components in understanding networks with inherent uncertainties.
    • The developed finite mixture model and expectation-maximization algorithm provide a robust method for motif discovery in stochastic biological networks.
    • The findings contribute to a deeper understanding of the organizational principles governing biological systems.