Integration of in vitro and in silico Models Using Bayesian Optimization With an Application to Stochastic Modeling of Mesenchymal 3D Cell Migration

• Published in: Frontiers in physiology Vol. 9; p. 1246
• Main Authors: Merino-Casallo, Francisco, Gomez-Benito, Maria J, Juste-Lanas, Yago, Martinez-Cantin, Ruben, Garcia-Aznar, Jose M
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 11-09-2018
• Subjects: 3D mesenchymal migration; chemotaxis; fibroblast; phosphoinositide 3-kinase; Physiology; platelet derived growth factor; tau-leaping algorithm; fibroblast; platelet derived growth factor; tau-leaping algorithm; 3D mesenchymal migration; Bayesian optimization; in-vitro in-silico integration; phosphoinositide 3-kinase; chemotaxis
• ISSN: 1664-042X; 1664-042X
• DOI: 10.3389/fphys.2018.01246

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
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
analysis of migrating fibroblasts embedded in 3D collagen-based fibrous matrices (2 mg/ml). These
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
model is replicating
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
experiments.