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

Abstract

In silico 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, 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 the lack of a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Here, we present PhysiCOOL, an open-source Python library tailored to create standardized calibration and optimization routines for PhysiCell models.

Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model

“…Additionally, some models include other aspects important to cancer biology such as evolution and the extracellular matrix ( 27 , 28 ). Even while techniques are being developed to calibrate these computationally expensive, stochastic models to real-world data ( 29 – 31 ), ABMs are situated to integrate domain expertise and bioinformatics analyses in a unified framework that can both generate and test hypotheses to advance basic and translational science ( 32 ).…”