Show simple item record

dc.contributor.authorRuíz Arrebola, Samuel
dc.contributor.authorGuirado Llorente, Damián 
dc.contributor.authorVillalobos Torres, Mercedes 
dc.contributor.authorLallena Rojo, Antonio Miguel 
dc.date.accessioned2021-06-07T08:27:46Z
dc.date.available2021-06-07T08:27:46Z
dc.date.issued2021
dc.identifier.citationRuiz-Arrebola, S.; Guirado, D.; Villalobos, M.; Lallena A.M. Evaluation of Classical Mathematical Models of Tumor Growth Using an On-Lattice Agent-Based Monte Carlo Model. Appl. Sci. 2021, 11, 5241. https://doi.org/10.3390/app11115241es_ES
dc.identifier.urihttp://hdl.handle.net/10481/69011
dc.description.abstractPurpose: To analyze the capabilities of different classical mathematical models to describe the growth of multicellular spheroids simulated with an on-lattice agent-based Monte Carlo model that has already been validated. Methods: The exponential, Gompertz, logistic, potential, and Bertalanffy models have been fitted in different situations to volume data generated with a Monte Carlo agent-based model that simulates the spheroid growth. Two samples of pseudo-data, obtained by assuming different variability in the simulation parameters, were considered. The mathematical models were fitted to the whole growth curves and also to parts of them, thus permitting to analyze the predictive power (both prospective and retrospective) of the models. Results: The consideration of the data obtained with a larger variability of the simulation parameters increases the width of the χ 2 distributions obtained in the fits. The Gompertz model provided the best fits to the whole growth curves, yielding an average value of the χ 2 per degree of freedom of 3.2, an order of magnitude smaller than those found for the other models. Gompertz and Bertalanffy models gave a similar retrospective prediction capability. In what refers to prospective prediction power, the Gompertz model showed by far the best performance. Conclusions: The classical mathematical models that have been analyzed show poor prediction capabilities to reproduce the MTS growth data not used to fit them. Within these poor results, the Gompertz model proves to be the one that better describes the growth data simulated. The simulation of the growth of tumors or multicellular spheroids permits to have follow-up periods longer than in the usual experimental studies and with a much larger number of samples: this has permitted performing the type of analysis presented here.es_ES
dc.description.sponsorshipSpanish Ministerio de Ciencia y Competitividad (FPA2015-67694-P, PID2019-104888GB-I00)es_ES
dc.description.sponsorshipEuropean Regional Development Fund (ERDF)es_ES
dc.description.sponsorshipJunta de Andalucía (FQM0387, P18-RT-3237)es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectOn-lattice agent-based modelses_ES
dc.subjectClassical tumor growth modelses_ES
dc.subjectExponentiales_ES
dc.subjectGompertzes_ES
dc.subjectLogistics es_ES
dc.subjectBertalanffyes_ES
dc.subjectMulticellular spheroidses_ES
dc.subjectMonte Carloes_ES
dc.titleEvaluation of Classical Mathematical Models of Tumor Growth Using an On-Lattice Agent-Based Monte Carlo Modeles_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/app11115241


Files in this item

[PDF]

This item appears in the following Collection(s)

Show simple item record

Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España