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Reducing Boolean networks with backward equivalence.

Georgios A Argyris1, Alberto Lluch Lafuente1, Mirco Tribastone2

  • 1Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.

BMC Bioinformatics
|May 23, 2023
PubMed
Summary
This summary is machine-generated.

Boolean Networks (BNs) face state space explosion challenges. Boolean Backward Equivalence (BBE) reduces model complexity by collapsing equivalent variables, enabling faster analysis and preserving essential biological dynamics.

Keywords:
Attractors analysisBoolean networkModel reductionPartition refinementState-space generation

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Dynamical Systems Theory

Background:

  • Boolean Networks (BNs) are widely used dynamical models in biology.
  • BNs represent component states as binary values (e.g., activation/deactivation).
  • The state space explosion in BNs hinders their comprehensive analysis.

Purpose of the Study:

  • To introduce Boolean Backward Equivalence (BBE), a novel reduction technique for BNs.
  • To address the state space explosion problem in Boolean Network analysis.
  • To enhance the tractability and speed of BN model analysis.

Main Methods:

  • Developed Boolean Backward Equivalence (BBE) to collapse equivalent system variables.
  • Validated BBE on 86 models from online repositories.
  • Tuned BBE's reduction power using model-specific information for targeted analysis.

Main Results:

  • BBE effectively reduced over 90% of the validated models.
  • Significant speed-ups were observed in state space generation and steady-state analysis.
  • BBE enabled the analysis of previously intractable models.
  • Case studies demonstrated tuning BBE to preserve relevant biological dynamics.

Conclusions:

  • BBE complements existing BN reduction methods by preserving unique properties.
  • It selectively removes dynamics not originating from specific initial conditions, preserving essential behaviors.
  • BBE ensures exact preservation of remaining dynamics, including attractor length and reachability.
  • BBE is a model-to-model reduction technique compatible with other methods.