000118900 001__ 118900
000118900 005__ 20240319081003.0
000118900 0247_ $$2doi$$a10.1016/j.jtbi.2022.111173
000118900 0248_ $$2sideral$$a129434
000118900 037__ $$aART-2022-129434
000118900 041__ $$aeng
000118900 100__ $$aMelo Quintela, Bárbara de
000118900 245__ $$aA theoretical analysis of the scale separation in a model to predict solid tumour growth
000118900 260__ $$c2022
000118900 5060_ $$aAccess copy available to the general public$$fUnrestricted
000118900 5203_ $$aSolid tumour growth depends on a host of factors which affect the cell life cycle and extracellular matrix vascularization that leads to a favourable environment. The whole solid tumour can either grow or wither in response to the action of the immune system and therapeutics. A personalised mathematical model of such behaviour must consider both the intra- and inter-cellular dynamics and the mechanics of the solid tumour and its microenvironment. However, such wide range of spatial and temporal scales can hardly be modelled in a single model, and require the so-called multiscale models, defined as orchestrations of single-scale component models, connected by relation models that transform the data for one scale to another. While multiscale models are becoming common, there is a well-established engineering approach to the definition of the scale separation, e.g., how the spatiotemporal continuum is split in the various component models. In most studies scale separation is defined as natural, linked to anatomical concepts such as organ, tissue, or cell; but these do not provide reliable definition of scales: for examples skeletal organs can be as large as 500 mm (femur), or as small as 3 mm (stapes). Here we apply a recently proposed scale-separation approach based on the actual experimental and computational limitations to a patient-specific model of the growth of neuroblastoma. The resulting multiscale model can be properly informed with the available experimental data and solved in a reasonable timeframe with the available computational resources.
000118900 536__ $$9info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE$$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-094494-B-C21
000118900 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000118900 590__ $$a2.0$$b2022
000118900 592__ $$a0.566$$b2022
000118900 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b34 / 55 = 0.618$$c2022$$dQ3$$eT2
000118900 593__ $$aAgricultural and Biological Sciences (miscellaneous)$$c2022$$dQ1
000118900 591__ $$aBIOLOGY$$b54 / 92 = 0.587$$c2022$$dQ3$$eT2
000118900 593__ $$aBiochemistry, Genetics and Molecular Biology (miscellaneous)$$c2022$$dQ2
000118900 593__ $$aStatistics and Probability$$c2022$$dQ2
000118900 593__ $$aMedicine (miscellaneous)$$c2022$$dQ2
000118900 593__ $$aModeling and Simulation$$c2022$$dQ2
000118900 593__ $$aApplied Mathematics$$c2022$$dQ2
000118900 593__ $$aImmunology and Microbiology (miscellaneous)$$c2022$$dQ2
000118900 594__ $$a4.9$$b2022
000118900 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000118900 700__ $$0(orcid)0000-0002-9864-7683$$aHervás Raluy, Silvia$$uUniversidad de Zaragoza
000118900 700__ $$0(orcid)0000-0001-8324-5596$$aGarcía Aznar, José Manuel$$uUniversidad de Zaragoza
000118900 700__ $$aWalker, Dawn
000118900 700__ $$aWertheim, Kenneth Y.
000118900 700__ $$aViceconti, Marco
000118900 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000118900 773__ $$g547 (2022), [7 pp.]$$pJ. theor. biol.$$tJournal of theoretical biology$$x0022-5193
000118900 8564_ $$s602557$$uhttps://zaguan.unizar.es/record/118900/files/texto_completo.pdf$$yVersión publicada
000118900 8564_ $$s2670068$$uhttps://zaguan.unizar.es/record/118900/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000118900 909CO $$ooai:zaguan.unizar.es:118900$$particulos$$pdriver
000118900 951__ $$a2024-03-18-14:23:46
000118900 980__ $$aARTICLE