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000128030 005__ 20241125101152.0
000128030 0247_ $$2doi$$a10.1007/s11538-023-01194-9
000128030 0248_ $$2sideral$$a135179
000128030 037__ $$aART-2023-135179
000128030 041__ $$aeng
000128030 100__ $$0(orcid)0000-0003-2564-6038$$aAyensa-Jiménez, Jacobo
000128030 245__ $$aA Mathematical Modelling Study of Chemotactic Dynamics in Cell Cultures: The Impact of Spatio-temporal Heterogeneity
000128030 260__ $$c2023
000128030 5060_ $$aAccess copy available to the general public$$fUnrestricted
000128030 5203_ $$aAs motivated by studies of cellular motility driven by spatiotemporal chemotactic gradients in microdevices, we develop a framework for constructing approximate analytical solutions for the location, speed and cellular densities for cell chemotaxis waves in heterogeneous fields of chemoattractant from the underlying partial differential equation models. In particular, such chemotactic waves are not in general translationally invariant travelling waves, but possess a spatial variation that evolves in time, and may even oscillate back and forth in time, according to the details of the chemotactic gradients. The analytical framework exploits the observation that unbiased cellular diffusive flux is typically small compared to chemotactic fluxes and is first developed and validated for a range of exemplar scenarios. The framework is subsequently applied to more complex models considering the chemoattractant dynamics under more general settings, potentially including those of relevance for representing pathophysiology scenarios in microdevice studies. In particular, even though solutions cannot be constructed in all cases, a wide variety of scenarios can be considered analytically, firstly providing global insight into the important mechanisms and features of cell motility in complex spatiotemporal fields of chemoattractant. Such analytical solutions also provide a means of rapid evaluation of model predictions, with the prospect of application in computationally demanding investigations relating theoretical models and experimental observation, such as Bayesian parameter estimation.
000128030 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-106099RB-C44
000128030 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000128030 590__ $$a2.0$$b2023
000128030 592__ $$a0.61$$b2023
000128030 591__ $$aBIOLOGY$$b49 / 109 = 0.45$$c2023$$dQ2$$eT2
000128030 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b34 / 66 = 0.515$$c2023$$dQ3$$eT2
000128030 593__ $$aAgricultural and Biological Sciences (miscellaneous)$$c2023$$dQ1
000128030 593__ $$aEnvironmental Science (miscellaneous)$$c2023$$dQ2
000128030 593__ $$aPharmacology$$c2023$$dQ2
000128030 593__ $$aBiochemistry, Genetics and Molecular Biology (miscellaneous)$$c2023$$dQ2
000128030 593__ $$aComputational Theory and Mathematics$$c2023$$dQ2
000128030 593__ $$aMathematics (miscellaneous)$$c2023$$dQ2
000128030 593__ $$aNeuroscience (miscellaneous)$$c2023$$dQ3
000128030 593__ $$aImmunology$$c2023$$dQ3
000128030 594__ $$a3.9$$b2023
000128030 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000128030 700__ $$0(orcid)0000-0003-0088-7222$$aDoweidar, Mohamed H.$$uUniversidad de Zaragoza
000128030 700__ $$0(orcid)0000-0001-8741-6452$$aDoblaré, Manuel$$uUniversidad de Zaragoza
000128030 700__ $$aGaffney, Eamonn A.
000128030 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000128030 773__ $$g85, 98 (2023), [48 pp.]$$pBull. math. biol.$$tBULLETIN OF MATHEMATICAL BIOLOGY$$x0092-8240
000128030 8564_ $$s2823685$$uhttps://zaguan.unizar.es/record/128030/files/texto_completo.pdf$$yVersión publicada
000128030 8564_ $$s1210551$$uhttps://zaguan.unizar.es/record/128030/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000128030 909CO $$ooai:zaguan.unizar.es:128030$$particulos$$pdriver
000128030 951__ $$a2024-11-22-12:07:36
000128030 980__ $$aARTICLE