PhysiCOOL

PhysiCOOL provides a Python library for generalized parameter space exploration, calibration, and optimization of PhysiCell agent-based models to constrain physical and biological parameters using experimental data.

Key Features:

• Python library: Implements computational routines as a Python library for integration with PhysiCell models.
• Parameter space exploration: Enables systematic exploration of parameter spaces comprising physical and biological properties of models.
• Calibration and optimization routines: Supplies standardized routines specifically tailored for calibrating and optimizing PhysiCell models.
• Integration with PhysiCell: Connects calibration and exploration workflows to PhysiCell agent-based simulations across temporal and spatial scales.
• Experimental-data constraints: Leverages experimental data to constrain parameter ranges during calibration.
• Parameter validation support: Facilitates characterization and validation of model parameters to improve biological relevance of simulations.

Scientific Applications:

• Model calibration: Calibrates PhysiCell agent-based models against experimental measurements.
• Parameter characterization: Characterizes multi-dimensional parameter spaces for physical and biological properties.
• Model validation: Validates in silico models by identifying parameter sets that produce biologically meaningful behavior.
• Parameter optimization: Optimizes model parameters to improve simulation fidelity.
• Computational experiments: Supports systematic computational experiments to assess model behavior under varied parameterizations.

Methodology:

Constrains parameter space using experimental data and applies standardized calibration and optimization routines to PhysiCell agent-based models.