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

Activities and sensitivities in boolean network models.

Ilya Shmulevich1, Stuart A Kauffman

  • 1Cancer Genomics Laboratory, University of Texas M. D. Anderson Cancer Center, Houston, Texas 77030, USA.

Physical Review Letters
|August 25, 2004
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

Sequential transcriptional waves and NF-κB-driven chromatin remodeling direct drug-induced dedifferentiation in cancer.

Nature communications·2026
Same author

Cellular heterogeneity and therapeutic response profiling of human IDH + glioma stem cell cultures.

Scientific reports·2025
Same author

Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemia.

NPJ digital medicine·2025
Same author

Cellular heterogeneity and therapeutic response profiling of human IDH+ glioma stem cell cultures.

bioRxiv : the preprint server for biology·2025
Same author

Medical digital twins: enabling precision medicine and medical artificial intelligence.

The Lancet. Digital health·2025
Same author

Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.

Cancer cell·2025
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Variable importance and function sensitivity significantly influence Boolean network dynamics. Higher average sensitivity in random Boolean networks predicts critical transitions, highlighting its role in network behavior.

Area of Science:

  • Computational Biology
  • Network Science
  • Boolean Dynamics

Background:

  • Boolean networks are models for complex systems.
  • Understanding variable importance and sensitivity is crucial for predicting network behavior.
  • Existing research lacks a unified framework connecting these measures to network dynamics.

Purpose of the Study:

  • To investigate the impact of variable importance and sensitivity on Boolean network dynamics.
  • To establish a connection between average sensitivity and critical transitions in random Boolean networks.
  • To explore the role of canalizing functions and their variables in network importance.

Main Methods:

  • Analysis of variable activity as a measure of importance.
  • Calculation of average sensitivity for Boolean functions.

Related Experiment Videos

  • Examination of random Boolean networks and their critical transition curves.
  • Comparison of importance measures for canalizing versus noncanalizing variables.

    • Main Results:

    • Expected average sensitivity determines the critical transition curve in random Boolean networks.
    • Canalizing variables exhibit higher importance (activity) than noncanalizing variables.
    • Average sensitivity plays a key role in dictating Boolean network dynamical behavior.

    Conclusions:

    • Variable importance and average sensitivity are critical determinants of Boolean network dynamics.
    • Average sensitivity provides a predictive measure for critical transitions.
    • Canalizing functions contribute to higher variable importance within networks.