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An open problem: Why are motif-avoidant attractors so rare in asynchronous Boolean networks?

Samuel Pastva1,2, Kyu Hyong Park3, Ondřej Huvar4

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Summary
This summary is machine-generated.

Motif-avoidant attractors (MAAs) are rare in biological systems but emerge after network simplification. These attractors are fragile and easily disrupted by linear extensions, with new bounds established for their disruption.

Keywords:
Biomolecular networksBoolean modelsBoolean networksComplex systemsDiscrete dynamicsStable motifTrap spaces

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Dynamical Systems Theory

Background:

  • Boolean networks model biological phenotypes as attractors, often linked to minimal trap spaces.
  • Motif-avoidant attractors (MAAs) exist outside minimal trap spaces and are poorly understood.
  • Understanding MAAs is crucial for accurate modeling of biological system dynamics.

Purpose of the Study:

  • To summarize current knowledge on MAAs in asynchronous Boolean networks.
  • To investigate the emergence and disruption of MAAs under network reduction and linear extensions.
  • To provide novel computational insights into the behavior of MAAs.

Main Methods:

  • Analysis of 14,000 biological Boolean models and over 100 million Random Boolean Networks.
  • Computational studies on the effects of node deletion and edge linear extensions on MAAs.
  • Derivation of improved upper bounds for disrupting MAAs.

Main Results:

  • MAAs were observed in biological models primarily after applying simplification methods, indicating network reduction's role in their emergence.
  • MAAs are fragile to linear extensions, with sparse networks showing high susceptibility.
  • A single linear node can disrupt nearly all MAAs in sparse networks.

Conclusions:

  • Network simplification plays a key role in the appearance of MAAs in biological models.
  • MAAs are sensitive to perturbations, particularly linear extensions in sparse networks.
  • The study provides a better understanding of MAA dynamics and their disruption, refining theoretical bounds.