BoolDog

BoolDog supports the construction, simulation, and analysis of Boolean and semi-quantitative Boolean network models for logic-based modeling of complex biological systems.

Key Features:

• Network I/O: Imports and exports networks across standard formats including SBML-qual, GraphML, bnet, SIF, and Cytoscape.
• Boolean simulation: Performs synchronous simulation with events, attractor identification, and steady-state identification.
• Semi-quantitative relaxation: Transforms logic-based models into continuous ordinary differential equation (ODE) systems via ODE-based continuous/semi-quantitative relaxations of Boolean models.
• Prime-implicant-based Boolean algebra: Wraps pyboolnet for prime-implicant-based Boolean algebra operations.
• Simulation-result plotting: Generates plots of simulation results.
• Python library integration: Integrates natively with NetworkX, igraph, and py4Cytoscape for network analysis and visualization.

Scientific Applications:

• Complex biological systems analysis: Enables logic-based modeling of biological pathways when detailed biochemical and kinetic information is unavailable.
• Discrete-continuous modeling integration: Facilitates seamless integration of discrete Boolean and continuous ODE modeling paradigms for biological networks.
• Network topology and dynamics: Supports analysis of system components and their interactions through simplified yet informative abstractions of system behavior.

Methodology:

BoolDog wraps pyboolnet for prime-implicant-based Boolean algebra and implements synchronous simulation with events, attractor and steady-state identification, and systematic transformation of logic-based models into continuous ordinary differential equation (ODE) systems.