Abstract

In silico models of biological systems are usually very complex and rely on several parameters describing physical and biological properties that require validation. As such, parameter space exploration is an essential component of computational model development to fully characterize and validate simulation results. Experimental data may also be used to constrain parameter space (or enable model calibration) to enhance the biological relevance of model parameters. One widely used computational platform in the mathematical biology community is PhysiCell which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. Nonetheless, one limitation of PhysiCell is that there has not been a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Taking this into account, we present PhysiCOOL, an open-source Python library tailored to create standardized calibration and optimization routines of PhysiCell models. Graphical abstract