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

Gonçalves, Ines G. ; Hormuth, David A. ; Prabhakaran ; Sandhya ; Phillips, Caleb M. ; García-Aznar, José Manuel (Universidad de Zaragoza)

Resumen: 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

Idioma: Inglés

DOI: 10.46471/gigabyte.77

Año: 2023

Publicado en: GigaByte 2023 (2023), 1-11
ISSN: 2709-4715

Factor impacto CITESCORE: 2.6 - Biotechnology (Q3) - Biochemistry, Genetics and Molecular Biology (miscellaneous) (Q3) - Molecular Medicine (Q4) - Genetics (Q4)

Financiación: info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS
Financiación: info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)