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

Iterated maps for annealed Boolean networks.

Juha Kesseli1, Pauli Rämö, Olli Yli-Harja

  • 1Institute of Signal Processing, Tampere University of Technology, 33101 Tampere, Finland. kesseli@cs.tut.fi

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 13, 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

Mapping the future of medicine through digital twins.

Frontiers in molecular medicine·2026
Same author

The role of digital twins in P4 medicine: A paradigm for modern healthcare.

NPJ digital medicine·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

Comparative analysis of the antiglioblastoma activity of adenosine A<sub>2A</sub> receptor and CK1δ blockers.

European journal of pharmacology·2025
Same author

Identification of protein-coding genes associated with metastatic prostate cancer.

Endocrine-related cancer·2025
Same author

A<sub>2A</sub> receptor antagonist 4-(2-((6-Amino-9-ethyl-8-(furan-2-yl)-9H-purin-2-yl)amino)ethyl)phenol, a promising adenosine derivative for glioblastoma treatment.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences·2025
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

This study clarifies variations in annealed approximations for Boolean networks, crucial for modeling genetic regulation. The new four-state model explicitly details these differences, improving analysis of complex biological systems.

Area of Science:

  • Computational Biology
  • Nonlinear Dynamics
  • Statistical Physics

Background:

  • Boolean networks are widely used for modeling large-scale nonlinear systems, particularly genetic regulatory networks.
  • The annealed approximation is a common statistical method for analyzing the dynamics of Boolean networks.
  • Existing literature presents multiple, sometimes conflicting, variations of the annealed approximation due to differing underlying assumptions.

Purpose of the Study:

  • To introduce a unified framework, the four-state model, for understanding different annealed approximations.
  • To explicitly derive and differentiate various annealed approximations from this foundational model.
  • To clarify the interconnections and distinct assumptions of these approximations.

Main Methods:

Related Experiment Videos

  • Development of a novel four-state model.
  • Derivation of multiple annealed approximations from the four-state model.
  • Comparative analysis of the derived approximations.

    • Main Results:

    • The four-state model provides a unified basis for understanding diverse annealed approximations.
    • Explicit derivations reveal the specific assumptions and limitations of each approximation.
    • The study elucidates the relationships and distinctions between different approximation methods.

    Conclusions:

    • The presented four-state model offers a clear framework for selecting and applying appropriate annealed approximations.
    • This work enhances the accurate dynamical analysis of Boolean networks, especially in genetic regulatory network modeling.
    • The findings facilitate a more rigorous study of Boolean networks constructed with various function types.