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Boolean function metrics can assist modelers to check and choose logical rules.

John Zobolas1, Pedro T Monteiro2, Martin Kuiper1

  • 1Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Journal of Theoretical Biology
|January 27, 2022
PubMed
Summary

Boolean function metrics help analyze computational biology models. New metrics assess rule consistency and bias, improving model plausibility and guiding operator selection in complex biological networks.

Keywords:
BiasBoolean functionsBoolean regulatory networksComplexityTruth Density

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Computational models are crucial for analyzing complex biological systems.
  • Logic-based modeling enhances understanding but defining regulatory rules is challenging due to data noise and model complexity.
  • Automated tools are essential for assembling rules in large-scale models.

Purpose of the Study:

  • To introduce Boolean function metrics for analyzing model parameterization impacts.
  • To link semantic characterization of Boolean functions with regulatory structure consistency.
  • To evaluate the biological plausibility and asymptotic behavior of regulatory functions.

Main Methods:

  • Development and application of novel Boolean function metrics.
  • Analysis of function consistency with underlying regulatory structures.
  • Investigation of function bias and asymptotic output behavior.
  • Graph analysis of Boolean functions with varying numbers of regulators.

Main Results:

  • Several Boolean functions violate consistency properties, questioning their biological plausibility.
  • Regulatory functions exhibit diverse asymptotic behaviors, with some biased towards specific outcomes.
  • Function bias can guide the selection of logical operators in signaling cancer networks.
  • Increasing regulators amplify the bias of commonly used Boolean functions.

Conclusions:

  • Boolean function metrics provide a framework for assessing model parameterization and biological plausibility.
  • Rule specification significantly influences regulatory outcomes, even in complex biological networks.
  • Understanding function bias is key for developing accurate and reliable computational models in systems biology.