000075511 001__ 75511
000075511 005__ 20210820090344.0
000075511 0247_ $$2doi$$a10.3389/fphys.2018.01246
000075511 0248_ $$2sideral$$a107989
000075511 037__ $$aART-2018-107989
000075511 041__ $$aeng
000075511 100__ $$0(orcid)0000-0003-3852-0987$$aMerino-Casallo, F.$$uUniversidad de Zaragoza
000075511 245__ $$aIntegration of in vitro and in silico Models Using Bayesian Optimization With an Application to Stochastic Modeling of Mesenchymal 3D Cell Migration
000075511 260__ $$c2018
000075511 5060_ $$aAccess copy available to the general public$$fUnrestricted
000075511 5203_ $$aCellular migration plays a crucial role in many aspects of life and development. In this paper, we propose a computational model of 3D migration that is solved by means of the tau-leaping algorithm and whose parameters have been calibrated using Bayesian optimization. Our main focus is two-fold: to optimize the numerical performance of the mechano-chemical model as well as to automate the calibration process of in silico models using Bayesian optimization. The presented mechano-chemical model allows us to simulate the stochastic behavior of our chemically reacting system in combination with mechanical constraints due to the surrounding collagen-based matrix. This numerical model has been used to simulate fibroblast migration. Moreover, we have performed in vitro analysis of migrating fibroblasts embedded in 3D collagen-based fibrous matrices (2 mg/ml). These in vitro experiments have been performed with the main objective of calibrating our model. Nine model parameters have been calibrated testing 300 different parametrizations using a completely automatic approach. Two competing evaluation metrics based on the Bhattacharyya coefficient have been defined in order to fit the model parameters. These metrics evaluate how accurately the in silico model is replicating in vitro measurements regarding the two main variables quantified in the experimental data (number of protrusions and the length of the longest protrusion). The selection of an optimal parametrization is based on the balance between the defined evaluation metrics. Results show how the calibrated model is able to predict the main features observed in the in vitro experiments.
000075511 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/DPI2015-65962-R$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2015-64221-C2-1-R$$9info:eu-repo/grantAgreement/ES/MINECO/BES-2016-076291$$9info:eu-repo/grantAgreement/EC/FP7/306571/EU/Predictive modelling and simulation in mechano-chemo-biology: a computer multi-approach/INSILICO-CELL
000075511 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000075511 590__ $$a3.201$$b2018
000075511 591__ $$aPHYSIOLOGY$$b25 / 81 = 0.309$$c2018$$dQ2$$eT1
000075511 592__ $$a1.153$$b2018
000075511 593__ $$aPhysiology (medical)$$c2018$$dQ2
000075511 593__ $$aPhysiology$$c2018$$dQ2
000075511 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000075511 700__ $$0(orcid)0000-0002-1878-8997$$aGomez-Benito, M.J.$$uUniversidad de Zaragoza
000075511 700__ $$0(orcid)0000-0003-2237-8859$$aJuste-Lanas, Y.$$uUniversidad de Zaragoza
000075511 700__ $$0(orcid)0000-0002-6741-844X$$aMartinez-Cantin, R.
000075511 700__ $$0(orcid)0000-0002-9864-7683$$aGarcia-Aznar, J.M.$$uUniversidad de Zaragoza
000075511 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000075511 7102_ $$11002$$2050$$aUniversidad de Zaragoza$$bDpto. Bioq.Biolog.Mol. Celular$$cÁrea Biología Celular
000075511 773__ $$g9 (2018), 1246 [17 pp.]$$pFront. physiol.$$tFRONTIERS IN PHYSIOLOGY$$x1664-042X
000075511 8564_ $$s630215$$uhttps://zaguan.unizar.es/record/75511/files/texto_completo.pdf$$yVersión publicada
000075511 8564_ $$s11460$$uhttps://zaguan.unizar.es/record/75511/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
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000075511 951__ $$a2021-08-20-08:37:23
000075511 980__ $$aARTICLE