Model Parameter identification using 2D vs 3D experimental data: a comparative analysis

Abstract

Computational models are becoming an increasingly valuable tool in biomedical research. They enable the quantification of variables difficult to measure experimentally, an increase in the spatio-temporal resolution of the experiments and the testing of hypotheses. Parameter estimation from in-vitro data, remains a challenge, due to the limited availability of experimental datasets acquired in directly comparable conditions. While the use of computational models to supplement laboratory results contributes to this issue, a more extensive analysis of the effect of incomplete or inaccurate data on the parameter optimization process and its results is warranted. To this end, we compared the results obtained from the same in-silico model of ovarian cancer cell growth and metastasis, calibrated with datasets acquired from two different experimental settings: a traditional 2D monolayer, and 3D cell culture models. The differential behaviour of these models will inform the role and importance of experimental data in the calibration of computational models calibration. This work will also provide a set of general guidelines for the comparative testing and selection of experimental models and protocols to be used for parameter optimization in computational models Author summaryParameter identification is a key step in the development of a computational model, that is used to establish a connection between the simulated and experimental results and verify the accuracy of the in-silico framework. The selection of the in-vitro data to be used in this phase is fundamental, but little attention has been paid to the role of the experimental model in this process. To bridge this gap we present a comparative analysis of the same computational model calibrated using experimental data acquired from cells cultured (i) in 2D monolayers, (ii) in 3D culture models and (iii) a combination of the two. Data acquired in different experimental settings induce changes in the optimal parameter sets and the corresponding computational models behaviour. This translates in a varying degree of accuracy during the validation procedure, when the simulated data are compared to experimental measurements not used during the calibration step. Overall, our work provides a workflow and a set of guidelines to select the most appropriate experimental setting for the calibration and validation of computational models.