pystablemotifs

pystablemotifs: Exhaustive attractor and control analysis for Boolean networks

pystablemotifs analyzes Boolean networks modeling gene regulatory and signaling networks by exhaustively identifying stable states (attractors) and implementing attractor control strategies.

Key Features:

• Exhaustive Attractor Identification: Implements a non-heuristic, exhaustive algorithm (Rozum et al., 2021) to identify all attractors in Boolean networks.
• Attractor Control Algorithms: Provides six attractor control algorithms, five novel, to drive network dynamics toward specified attractors from any initial condition.
• Synergistic Control Strategies: Enables simultaneous or combined application of multiple control algorithms.
• Performance Optimization: Improves computational efficiency for large-scale Boolean network analysis.

Scientific Applications:

• Systems Biology Modeling: Models gene regulatory networks and signaling pathways to characterize stable cellular states, including differentiation and disease phenotypes.
• Network Control and Design: Guides therapeutic intervention strategies, synthetic biology applications, and design of robust biological circuits through attractor control.

Methodology:

Systematically explores the Boolean network state space using a non-heuristic exhaustive search algorithm to enumerate all attractors, then applies attractor control algorithms to redirect system trajectories from arbitrary initial states toward selected attractors based on the identified attractor landscape.