A comparative analysis of 2D and 3D experimental data for the identification of the parameters of computational models.

ABSTRACT: Computational models are becoming an increasingly valuable tool in biomedical research. Their accuracy and effectiveness, however, rely on the identification of suitable parameters and on appropriate validation of the in-silico framework. Both these steps are highly dependent on the experimental model used as a reference to acquire the data. Selecting the most appropriate experimental framework thus becomes key, together with the analysis of the effect of combining results from different experimental models, a common practice often necessary due to limited data availability. In this work, the same in-silico model of ovarian cancer cell growth and metastasis, was calibrated with datasets acquired from traditional 2D monolayers, 3D cell culture models or a combination of the two. The compari

SUBMITTER: Cortesi M 

PROVIDER: S-EPMC10517149 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature