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Related Experiment Video

Updated: Sep 23, 2025

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Exploring attractor bifurcations in Boolean networks.

Nikola Beneš1, Luboš Brim2, Jakub Kadlecaj2

  • 1Faculty of Informatics, Masaryk University, Brno, Czechia. xbenes3@fi.muni.cz.

BMC Bioinformatics
|May 13, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new method to analyze bifurcations in large biological networks, revealing key parameters influencing system behavior and drug efficacy against SARS-CoV-2.

Keywords:
Attractor bifurcationBoolean networksSoftware toolSymbolic computationtype-1 interferons

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biophysics

Background:

  • Boolean networks (BNs) model complex biochemical systems, with attractors representing long-term behavior and phenotypes.
  • Bifurcations, or significant changes in attractors, occur due to variations in logical parameters.
  • Analyzing these bifurcations is crucial for understanding system dynamics.

Purpose of the Study:

  • To present a methodology for analyzing bifurcations in asynchronous parametrised Boolean networks.
  • To develop a computational framework for analyzing large-scale biological networks.
  • To investigate the suppressive role of GRL0617 on SARS-CoV-2 replication.

Main Methods:

  • Utilizing advanced symbolic graph algorithms for analyzing large networks (hundreds of Boolean variables).
  • Developing an interactive decision tree-based visualization technique to identify crucial parameters.
  • Implementing the methodology in a tool named AEON.

Main Results:

  • Successfully applied the methodology to a human cell signaling network involving type-1 interferons and SARS-CoV-2.
  • Identified parameters critical for changes in the attractor landscape.
  • Provided insights into the potential suppressive effect of GRL0617 on viral replication.

Conclusions:

  • The proposed method offers a robust approach to bifurcation analysis in large, complex biological networks, analogous to kinetic modeling.
  • AEON efficiently handles large networks with incomplete information, a significant advantage.
  • Case study results align with recent biological findings, validating the method's applicability.