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AEON.py: Python library for attractor analysis in asynchronous Boolean networks.

Nikola Beneš1, Luboš Brim1, Ondřej Huvar1

  • 1Faculty of Informatics, Masaryk University, Brno 602 00, Czech Republic.

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Summary
This summary is machine-generated.

AEON.py is a new Python library for analyzing large asynchronous Boolean networks. It offers faster attractor detection and analysis of uncertain networks, improving control strategy assessment.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Network Science

Background:

  • Asynchronous Boolean networks (ABNs) are crucial for modeling complex biological systems.
  • Analyzing the long-term behavior of large ABNs presents significant computational challenges.

Purpose of the Study:

  • To introduce AEON.py, a Python library for efficient analysis of large ABNs.
  • To improve computational performance for attractor detection in ABNs.
  • To enable analysis of partially specified ABNs and control strategies.

Main Methods:

  • Development of a Python library (AEON.py) leveraging optimized algorithms.
  • Implementation of techniques for attractor detection in large ABNs.
  • Inclusion of methods for analyzing partially specified networks and control strategies.

Main Results:

  • AEON.py provides significant computational improvements over existing methods for attractor detection.
  • The library facilitates the analysis of ABNs with uncertain update functions.
  • AEON.py enables identification and robustness assessment of source-target control strategies.

Conclusions:

  • AEON.py offers a powerful and efficient tool for studying the long-term dynamics of large ABNs.
  • The library enhances the analysis of complex biological regulatory networks.
  • AEON.py supports the development and evaluation of control strategies in biological systems.