Tumour growth: Bayesian parameter calibration of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment

Abstract

To unravel processes that lead to the growth of solid tumours, it is necessary to link knowledge of cancer biology with the physical properties of the tumour and its interaction with the surrounding microenvironment. Our understanding of the underlying mechanisms is however still imprecise. We therefore developed computational physics-based models, which incorporate the interaction of the tumour with its surroundings based on the theory of porous media. However, the experimental validation of such models represents a challenge to its clinical use as a prognostic tool. This study combines a physics-based model with in vitro experiments based on microfluidic devices used to mimic a 3D tumour microenvironment. By conducting a global sensitivity analysis, we identify the most influential input parameters and infer their posterior distribution based on Bayesian calibration. The resulting probability density is in agreement with the scattering of the experimental data and thus validates the modelling approach. Using the proposed workflow, we demonstrate that we can indirectly characterise the mechanical properties of neuroblastoma spheroids that cannot feasibly be measured experimentally.

Pressure‐ and time‐dependent alveolar recruitment/derecruitment in a spatially resolved patient‐specific computational model for injured human lungs

“…In this context, we have already presented novel and very promising Bayesian-based approaches that may be useful to identify the parameters for the present model. [57][58][59] Doing so enables both pure parameter optimization-maybe even for the full model without using the proposed calibration method to also integrate data from other measuring equipment-as well as considering and testing different distributions of the time constants τ, for example, quasi-hyperbolic, exponential, or lognormal, or the pathology-dependent design of variables like the time constants and tissue stiffnesses κ. 26 The latter goes beyond the scope of this study, but is a valid object of research as remodeling of tissue occurs already in an early stage of ARDS.…”

“…For the sake of completeness, before going into more detail about the calibration method, we would like to point out that there are less refined and not explicitly designed for our purpose, but still effective procedures for model parametrization, for example, Bayesian inverse analysis methods, which our group is currently developing also in the context of biomechanical problems. [57][58][59] These methods might be of particular interest when using data from further or different measurement sources that are not used in the calibration method described below.…”