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Balance between noise and information flow maximizes set complexity of network dynamics
Tuomo Mäki-Marttunen1, Juha Kesseli, Matti Nykter
1Department of Signal Processing, Tampere University of Technology, Tampere, Finland. tuomo.maki-marttunen@tut.fi
Plos One
|March 22, 2013
Summary
Random Boolean networks exhibit high complexity during their transient phase before reaching an attractor. Adding noise can tune these networks to a state of maximal complexity, balancing information flow and system dynamics.
Area of Science:
- Computational biology
- Complex systems theory
- Information theory
Background:
- Boolean networks are discrete models for biological systems like genetic and metabolic networks.
- Their simplicity provides a foundation for studying physical systems.
- Understanding network dynamics and complexity is crucial for biological insights.
Purpose of the Study:
- To investigate the complexity of random Boolean networks during their transient phase.
- To explore the role of noise in tuning network complexity.
- To identify conditions for achieving maximal complexity in Boolean networks.
Main Methods:
- Utilized set complexity, a measure of context-dependent information.
- Employed statistical complexity to further analyze network behavior.
- Introduced controlled noise into deterministic Boolean dynamics.
Main Results:
- Random Boolean networks display high complexity in their transient states before reaching attractors.
- Adding noise can tune networks to a regime of maximal complexity.
- Networks with Poisson degree distributions and near-critical networks can be tuned to maximal set complexity.
- Maximal complexity is achieved by balancing noise and state space contraction, potentially disrupting information flow.
Conclusions:
- Maximal complexity near state transitions may be a general phenomenon in physical systems.
- Noise can be beneficial, aiding systems in maintaining states with high information content.
- The study highlights the utility of noise in biological and physical systems for information processing.
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