000097381 001__ 97381
000097381 005__ 20240219135522.0
000097381 0247_ $$2doi$$a10.1038/s41598-020-78215-3
000097381 0248_ $$2sideral$$a121766
000097381 037__ $$aART-2020-121766
000097381 041__ $$aeng
000097381 100__ $$0(orcid)0000-0003-2564-6038$$aAyensa-Jiménez, J.$$uUniversidad de Zaragoza
000097381 245__ $$aMathematical formulation and parametric analysis of in vitro cell models in microfluidic devices: application to different stages of glioblastoma evolution
000097381 260__ $$c2020
000097381 5060_ $$aAccess copy available to the general public$$fUnrestricted
000097381 5203_ $$aIn silico models and computer simulation are invaluable tools to better understand complex biological processes such as cancer evolution. However, the complexity of the biological environment, with many cell mechanisms in response to changing physical and chemical external stimuli, makes the associated mathematical models highly non-linear and multiparametric. One of the main problems of these models is the determination of the parameters’ values, which are usually fitted for specific conditions, making the conclusions drawn difficult to generalise. We analyse here an important biological problem: the evolution of hypoxia-driven migratory structures in Glioblastoma Multiforme (GBM), the most aggressive and lethal primary brain tumour. We establish a mathematical model considering the interaction of the tumour cells with oxygen concentration in what is called the go or grow paradigm. We reproduce in this work three different experiments, showing the main GBM structures (pseudopalisade and necrotic core formation), only changing the initial and boundary conditions. We prove that it is possible to obtain versatile mathematical tools which, together with a sound parametric analysis, allow to explain complex biological phenomena. We show the utility of this hybrid “biomimetic in vitro-in silico” platform to help to elucidate the mechanisms involved in cancer processes, to better understand the role of the different phenomena, to test new scientific hypotheses and to design new data-driven experiments.
000097381 536__ $$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/PGC2018-097257-B-C31$$9info:eu-repo/grantAgreement/ES/MINECO-AEI-FEDER/PID2019-106099RB-C44$$9info:eu-repo/grantAgreement/ES/DGA-ISCIII-EDRF/CIBER-BBN
000097381 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000097381 590__ $$a4.379$$b2020
000097381 591__ $$aMULTIDISCIPLINARY SCIENCES$$b17 / 72 = 0.236$$c2020$$dQ1$$eT1
000097381 592__ $$a1.24$$b2020
000097381 593__ $$aMultidisciplinary$$c2020$$dQ1
000097381 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000097381 700__ $$0(orcid)0000-0002-7909-4446$$aPérez-Aliacar, M.
000097381 700__ $$0(orcid)0000-0001-7232-7588$$aRandelovic, T.
000097381 700__ $$0(orcid)0000-0003-0156-4230$$aOliván, S.$$uUniversidad de Zaragoza
000097381 700__ $$0(orcid)0000-0002-4220-2722$$aFernández, L.
000097381 700__ $$aSanz-Herrera, J.A.
000097381 700__ $$0(orcid)0000-0003-2410-5678$$aOchoa, I.$$uUniversidad de Zaragoza
000097381 700__ $$0(orcid)0000-0003-0088-7222$$aDoweidar, M.H.$$uUniversidad de Zaragoza
000097381 700__ $$0(orcid)0000-0001-8741-6452$$aDoblaré, M.$$uUniversidad de Zaragoza
000097381 7102_ $$11003$$2443$$aUniversidad de Zaragoza$$bDpto. Anatom.Histolog.Humanas$$cArea Histología
000097381 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000097381 773__ $$g10, 1 (2020), 21193 [21 pp]$$pSci. rep. (Nat. Publ. Group)$$tScientific reports (Nature Publishing Group)$$x2045-2322
000097381 8564_ $$s3945003$$uhttps://zaguan.unizar.es/record/97381/files/texto_completo.pdf$$yVersión publicada
000097381 8564_ $$s280507$$uhttps://zaguan.unizar.es/record/97381/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
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000097381 951__ $$a2024-02-19-13:52:10
000097381 980__ $$aARTICLE