Attractor Stability of Boolean networks under noise.
Byungjoon Min1,2,3, Jeehye Choi2, Reinhard Laubenbacher3
1Department of Physics, Chungbuk National University, Cheongju, Chungbuk 28644, Korea.
Arxiv
|September 22, 2025
Summary
Noise impacts Boolean network stability. Transition dynamics, not just basin sizes, determine attractor dominance under local noise, revealing crucial insights into complex system behavior.
Area of Science:
- Systems Biology
- Computational Biology
- Theoretical Neuroscience
Background:
- Boolean networks are widely used to model complex biological systems.
- Understanding attractor dynamics and stability is crucial for predicting system behavior.
- Noise is an inherent factor in biological systems that can significantly alter dynamics.
Purpose of the Study:
- To investigate the impact of noise on attractor stability and transition behaviors in Boolean networks.
- To develop a quantitative framework for assessing attractor stability and dominance under noisy conditions.
- To differentiate the effects of local versus global noise on network dynamics.
Main Methods:
- Construction of attractor matrices using single-node perturbations.
- Quantification of attractor stability based on dynamical structure.
- Analysis of noise-induced transition patterns under local and global perturbations.
Main Results:
- Attractors exhibit greater stability than predicted by basin sizes alone.
- Global perturbations are influenced by basin sizes, while local noise effects are dominated by transition patterns.
- Noise-induced transition dynamics offer an efficient and quantitative measure of attractor stability.
Conclusions:
- Dynamical structure plays a critical role in attractor stability within noisy Boolean networks.
- Transition dynamics provide a more accurate description of attractor behavior under stochastic perturbations.
- The proposed framework offers a novel approach to understanding complex system dynamics in the presence of noise.
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