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Probabilistic edge weights fine-tune Boolean network dynamics.

Dávid Deritei1,2, Nina Kunšič1, Péter Csermely1

  • 1Department of Molecular Biology, Institute of Biochemistry and Molecular Biology, Semmelweis University, Budapest, Hungary.

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This study introduces probabilistic edge-weight (PEW) operators to model noise in biological regulatory networks. These operators enhance Boolean models, improving predictions and enabling new modeling capabilities.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Network Science

Background:

  • Biological systems exhibit inherent noise, impacting experimental data and computational models.
  • Accurate modeling of regulatory interactions (network edges) is crucial for understanding biological systems.
  • Existing Boolean dynamical systems may not fully capture the consequential effects of noise on regulatory edges.

Purpose of the Study:

  • To propose a novel method for explicitly encoding edge-noise in Boolean dynamical systems.
  • To introduce probabilistic edge-weight (PEW) operators for more biologically meaningful modeling.
  • To provide a practical implementation within the BooleanNet framework.

Main Methods:

  • Development of probabilistic edge-weight (PEW) operators to introduce state-dependent noise.
  • Integration of PEW operators into the BooleanNet framework for Boolean dynamical systems.
  • Application of PEW operators in case studies to analyze their impact on system dynamics.

Main Results:

  • PEW operators introduce edge-weights via noise, dependent on the system's dynamical state.
  • Implementation in BooleanNet offers a user-friendly approach to incorporating noise.
  • Few PEW operators can fine-tune emergent dynamics and improve qualitative prediction accuracy in Boolean models.
  • PEW operators resolve non-biological behaviors arising from asynchronous vs. synchronous updates.

Conclusions:

  • PEW operators offer a powerful tool for enhancing the realism of Boolean dynamical models.
  • This method improves the accuracy of predictions for biological regulatory networks.
  • PEW operators facilitate modeling of complex cellular dynamics like learning and incorporate omics-derived edge-weights.