000135333 001__ 135333
000135333 005__ 20240731103422.0
000135333 0247_ $$2doi$$a10.46471/gigabyte.77
000135333 0248_ $$2sideral$$a138589
000135333 037__ $$aART-2023-138589
000135333 041__ $$aeng
000135333 100__ $$aGonçalves, Ines G.
000135333 245__ $$aPhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects
000135333 260__ $$c2023
000135333 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135333 5203_ $$aIn 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
000135333 536__ $$9info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101018587-ICoMICS$$9info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE
000135333 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000135333 594__ $$a2.6$$b2023
000135333 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135333 700__ $$aHormuth, David A.
000135333 700__ $$aPrabhakaran
000135333 700__ $$aSandhya
000135333 700__ $$aPhillips, Caleb M.
000135333 700__ $$0(orcid)0000-0002-9864-7683$$aGarcía-Aznar, José Manuel$$uUniversidad de Zaragoza
000135333 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000135333 773__ $$g2023 (2023), 1-11$$pGigaByte$$tGigaByte$$x2709-4715
000135333 8564_ $$s931676$$uhttps://zaguan.unizar.es/record/135333/files/texto_completo.pdf$$yVersión publicada
000135333 8564_ $$s2406554$$uhttps://zaguan.unizar.es/record/135333/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135333 909CO $$ooai:zaguan.unizar.es:135333$$particulos$$pdriver
000135333 951__ $$a2024-07-31-10:09:57
000135333 980__ $$aARTICLE