Supporting data for "PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects"

<i>In silico</i> models of biological systems are usually very complex and rely on a large number of parameters describing physical and biological properties that require validation. As such, exploration of parameter space 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 to model parameters. One widely used computational platform in the mathematical biology community is <i>PhysiCell</i> which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. One limitation of <i>PhysiCell</i>, however, 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 <i>PhysiCOOL,</i> an open-source Python library tailored to create standardized calibration and optimization routines of <i>PhysiCell</i> models.