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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Data availability: PhysiCOOL is available as a Python library distributed through PyPi. The source code is available on GitHub. All the examples presented here can be run on Gitpod through interactive Jupyter Notebooks. Documentation is available on ReadTheDocs.
Funding: This work was supported as part of the 2021 PhysiCell Hackathon (administrative supplement to Multiscale systems biology modeling to exploit tumor-stromal metabolic crosstalk in colorectal cancer, grant no 1U01CA232137). IGG was and JMGA were supported as part of projects that have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no 101018587) and the project PRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, empowered by imaging biomarkers), a Horizon 2020|RIA project (Topic SC1-DTH-07-2018), grant agreement no: 826494.
Minor text revisions and corrections. Added more information about code availability, documentation and user support. Funding information was updated.