Integration of in vitro and in silico Models Using Bayesian Optimization With an Application to Stochastic Modeling of Mesenchymal 3D Cell Migration
Financiación FP7 / Fp7 Funds
Resumen: Cellular 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.
Idioma: Inglés
DOI: 10.3389/fphys.2018.01246
Año: 2018
Publicado en: FRONTIERS IN PHYSIOLOGY 9 (2018), 1246 [17 pp.]
ISSN: 1664-042X

Factor impacto JCR: 3.201 (2018)
Categ. JCR: PHYSIOLOGY rank: 25 / 81 = 0.309 (2018) - Q2 - T1
Factor impacto SCIMAGO: 1.153 - Physiology (medical) (Q2) - Physiology (Q2)

Financiación: info:eu-repo/grantAgreement/EC/FP7/306571/EU/Predictive modelling and simulation in mechano-chemo-biology: a computer multi-approach/INSILICO-CELL
Financiación: info:eu-repo/grantAgreement/ES/MINECO/BES-2016-076291
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2015-64221-C2-1-R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2015-65962-R
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)
Área (Departamento): Área Biología Celular (Dpto. Bioq.Biolog.Mol. Celular)

Creative Commons Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace.

Exportado de SIDERAL (2021-08-20-08:37:23)

Este artículo se encuentra en las siguientes colecciones:

 Registro creado el 2018-10-18, última modificación el 2021-08-20

Versión publicada:
Valore este documento:

Rate this document:
(Sin ninguna reseña)