000125288 001__ 125288
000125288 005__ 20230323145803.0
000125288 0247_ $$2doi$$a10.1016/j.csbj.2023.01.044
000125288 0248_ $$2sideral$$a133060
000125288 037__ $$aART-2023-133060
000125288 041__ $$aeng
000125288 100__ $$aGonçalves, Inês G.
000125288 245__ $$aHybrid computational models of multicellular tumour growth considering glucose metabolism
000125288 260__ $$c2023
000125288 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125288 5203_ $$aCancer cells metabolize glucose through metabolic pathways that differ from those used by healthy and differentiated cells. In particular, tumours have been shown to consume more glucose than their healthy counterparts and to use anaerobic metabolic pathways, even under aerobic conditions. Nevertheless, scientists have still not been able to explain why cancer cells evolved to present an altered metabolism and what evolutionary advantage this might provide them. Experimental and computational models have been increasingly used in recent years to understand some of these biological questions. Multicellular tumour spheroids are effective experimental models as they replicate the initial stages of avascular solid tumour growth. Furthermore, these experiments generate data which can be used to calibrate and validate computational studies that aim to simulate tumour growth. Hybrid models are of particular relevance in this field of research because they model cells as individual agents while also incorporating continuum representations of the substances present in the surrounding microenvironment that may participate in intracellular metabolic networks as concentration or density distributions. Henceforth, in this review, we explore the potential of computational modelling to reveal the role of metabolic reprogramming in tumour growth.
000125288 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$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21
000125288 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000125288 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125288 700__ $$0(orcid)0000-0002-9864-7683$$aGarcía-Aznar, José Manuel$$uUniversidad de Zaragoza
000125288 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000125288 773__ $$g21 (2023), 1262-1271$$pComput. struct. biotechnol. j.$$tComputational and Structural Biotechnology Journal$$x2001-0370
000125288 8564_ $$s3932948$$uhttps://zaguan.unizar.es/record/125288/files/texto_completo.pdf$$yVersión publicada
000125288 8564_ $$s2210188$$uhttps://zaguan.unizar.es/record/125288/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
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000125288 951__ $$a2023-03-23-12:44:11
000125288 980__ $$aARTICLE