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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.
Bioinformatics (Oxford, England)
|September 14, 2022
Summary
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.
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.
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