AEON.py

AEON.py analyzes large-scale asynchronous Boolean networks to compute attractors, perform bifurcation and model-checking analyses, and evaluate control and reprogramming strategies for studying gene regulatory network dynamics.

Key Features:

• Network Formats: Supports loading and storing networks in SBML-qual, .bnet, and .aeon formats.
• Rust backend: Uses a Rust backend to improve computational efficiency and performance on large-scale models.
• Handling uncertainty: Accommodates partially specified networks and uncertain update functions.
• Attractor computation and bifurcation analysis: Computes attractors representing long-term behavior and performs bifurcation analysis to assess parameter-dependent dynamics.
• Model checking and property classification: Performs model checking and property classification using (H)CTL (Hierarchical Computation Tree Logic).
• Control and reprogramming strategies: Supports one-step, permanent, and temporary perturbations and identifies source–target control strategies with robustness assessment.

Scientific Applications:

• Systems biology: Characterizes dynamic behavior of gene regulatory networks and cellular processes.
• Bioinformatics: Analyzes large and complex network models for computational studies of biological systems.
• Disease modeling: Explores regulatory network dynamics relevant to disease states and transitions.
• Synthetic biology: Evaluates intervention and reprogramming strategies for engineered regulatory circuits.

Methodology:

Computational methods explicitly include a Rust backend for performance, attractor detection, bifurcation analysis, model checking with (H)CTL, handling of partially specified networks with uncertain update functions, and evaluation of control strategies including one-step, permanent, and temporary perturbations and source–target control robustness.