trapmvn

trapmvn extends trap-space analysis from Boolean networks to multi-valued networks (MVNs) to identify and characterize stable regions, attractors, and multi-level dynamics in biological network models.

Key Features:

• Generalization of Trap Spaces: Extends the concept of trap spaces from Boolean networks to multi-valued networks, enabling analysis of multiple activation levels.
• Theoretical Development: Implements theoretical frameworks that adapt principles of trap spaces to the multi-valued context.
• Analysis Methods: Provides analysis methods to identify and study trap spaces within MVNs while handling increased state complexity.
• Case Study Applicability: Demonstrated applicability through a realistic case study of biological system modeling.
• Time Efficiency Evaluation: Experimental evaluation on a large collection of real-world models demonstrated time-efficient performance for analyses.

Scientific Applications:

• Systems biology modeling: Modeling and analysis of biological regulatory networks with multiple activation levels using MVNs and trap spaces.
• Stability and attractor analysis: Characterizing system stability and identifying attractors by detecting trap spaces in MVNs.
• Multi-level dynamics exploration: Investigating dynamics of complex biological networks to understand multi-level activation behaviors.

Methodology:

Adapts trap-space theory from Boolean networks to MVNs and implements new algorithms and computational techniques in Python.