ASP-G

ASP-G simulates genetic regulatory networks (GRNs) using Boolean network models to compute attractors and explore alternative interaction rules and update schemes.

Key Features:

• Modularity: Built on Answer Set Programming (ASP) to provide a modular structure that enables modification and extension of model components.
• Declarative Framework: Employs a declarative programming paradigm in which researchers specify genes, interactions, and constraints rather than procedural steps.
• Flexibility in Assumptions: Allows testing of a wide range of interaction rules and update schemes instead of imposing fixed assumptions.
• Attractor Computation: Computes attractors of Boolean GRN models representing stable states or recurring patterns relevant to processes such as differentiation and homeostasis.
• Correctness and Validation: Validated by recapitulating known attractors reported in published studies.
• Efficiency: Trades some performance for declarative flexibility, enabling exploratory modeling across multiple assumptions.

Scientific Applications:

• Investigate Gene Interaction Dynamics: Explore how alternative interaction rules and update schemes affect network behavior and gene interaction outcomes.
• Identify Critical Network States: Use attractor computation to identify stable states and patterns relevant to cell cycle regulation, differentiation, and disease progression.
• Hypothesis Testing: Test hypotheses about gene interactions and network behavior under varied assumptions within a Boolean GRN framework.

Methodology:

ASP-G uses Answer Set Programming to simulate GRNs as Boolean network models by declaratively specifying genes, interactions, and update rules and computing attractors.