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Related Experiment Videos

Rhythmic and non-rhythmic attractors in asynchronous random Boolean networks.

E A Di Paolo1

  • 1School of Cognitive and Computing Sciences, University of Sussex, BN1 9QH, Brighton, UK. ezequiel@cogs.susx.ac.uk

Bio Systems
|April 20, 2001
PubMed
Summary

Asynchronous Random Boolean Networks (ARBNs) can model rhythmic phenomena, challenging the default assumption against their use. These networks may even offer stronger rhythmic modeling due to inherent asynchrony.

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Area of Science:

  • Complex Systems
  • Computational Biology
  • Dynamical Systems

Background:

  • Updating schemes in discrete systems (e.g., Boolean networks) critically influence dynamics.
  • Synchronous updating is often favored, but asynchronous updating is proposed for systems lacking clear synchronous drivers.

Purpose of the Study:

  • Investigate the suitability of Asynchronous Random Boolean Networks (ARBNs) for modeling rhythmic phenomena.
  • Address the question of whether random asynchronous updating is inherently unsuitable for rhythmic modeling.

Main Methods:

  • Defined measures of pseudo-periodicity and relaxed statistical constraints.
  • Employed a genetic algorithm guided by these measures to search for rhythmic ARBNs.
  • Applied statistical analysis to validate findings.

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Main Results:

  • Successfully identified ARBNs capable of modeling coordinated rhythmic phenomena.
  • Demonstrated that ARBNs can exhibit rhythmic behavior, potentially enhanced by their asynchrony.
  • Discovered non-stationary attractors lacking rhythm, suggesting they are more abundant than rhythmic attractors.

Conclusions:

  • ARBNs are viable models for rhythmic phenomena, contrary to default assumptions.
  • The inherent asynchrony in ARBNs can contribute to robust rhythmic modeling.
  • The methodology is adaptable for stricter pseudo-periodicity definitions and evolutionary search constraints.