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Temporary and permanent control of partially specified Boolean networks.

Luboš Brim1, Samuel Pastva1, David Šafránek1

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Summary

Controlling partially specified Boolean networks (PSBNs) is challenging due to model uncertainty. This study introduces a symbolic method and perturbation robustness metric to manage gene knock-out and over-expression, finding temporary and permanent perturbations more reliable than one-step approaches.

Keywords:
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Area of Science:

  • Computational Systems Biology
  • Network Modeling
  • Control Theory

Background:

  • Boolean networks (BNs) are widely used in systems biology but often underspecified due to limited data.
  • Partially specified Boolean networks (PSBNs) represent this uncertainty by encoding multiple candidate networks.
  • Controlling BNs aims to stabilize systems using perturbations, but PSBN control presents unique challenges.

Purpose of the Study:

  • To develop methods for controlling partially specified Boolean networks (PSBNs).
  • To address the state space explosion caused by model uncertainty in PSBNs.
  • To introduce and quantify a new metric, perturbation robustness, for evaluating control strategies under uncertainty.

Main Methods:

  • A fully symbolic methodology is proposed to compactly represent numerous system variants within PSBNs.
  • Variable perturbations (gene knock-out, over-expression) with one-step, temporary, and permanent time windows are considered.
  • Perturbation robustness is introduced and quantified to measure efficacy across model uncertainty.

Main Results:

  • The symbolic methodology efficiently handles the state space explosion in PSBNs.
  • Perturbation robustness effectively characterizes the performance of control strategies against model uncertainty.
  • Experimental results on real-world PSBN models demonstrate the scalability and efficiency of the proposed methods.

Conclusions:

  • One-step perturbations are significantly less robust than temporary and permanent perturbations for PSBN control.
  • The developed methods provide a robust framework for controlling complex biological systems with inherent uncertainties.
  • This work advances the field of systems biology by enabling more reliable model-based interventions.