GeneFAtt

GeneFAtt computes attractors in synchronous and asynchronous Boolean models of genetic regulatory networks to characterize dynamically stable patterns underlying processes such as cellular division, differentiation, and homeostasis.

Key Features:

• Synchronous attractor computation: Computes attractors in synchronous Boolean models using iterative methods combined with reduced order binary decision diagrams (ROBDD) for compact state-space representation.
• Asynchronous attractor derivation: Derives attractors for asynchronous Boolean network models by translating synchronous attractors into asynchronous Boolean translation functions.
• Feedback loop handling: Supports identification of attractors in networks containing positive and negative feedback loops.
• State-space reduction: Employs ROBDD to compactly represent and efficiently manipulate the Boolean network state space, reducing computational overhead relative to exhaustive state-space search.
• Performance comparison: Demonstrates improved computational speed compared to genYsis on empirical experimental systems.

Scientific Applications:

• GRN dynamics analysis: Identification of attractors in synchronous and asynchronous Boolean models to study dynamic behaviors of genetic regulatory networks.
• Cellular process investigation: Mapping attractors to biological processes such as cellular division, differentiation, and homeostasis to infer molecular mechanisms.
• Algorithm benchmarking: Comparison of attractor computation performance against tools such as genYsis on empirical systems.

Methodology:

Synchronous attractor computation uses iterative methods with reduced order binary decision diagrams (ROBDD), and asynchronous attractor derivation translates synchronous attractors into asynchronous Boolean translation functions.