Bayesian Calibration of a Computational Model of Tissue Expansion Based on a Porcine Animal Model

Abstract

Tissue expansion is a technique used clinically to grow skin in situ to correct large defects. Despite its enormous potential, the use of tissue expansion is limited by our inability to control skin growth at will. We lack fundamental knowledge of how skin adapts to mechanical cues, and we lack computational models to predict growth to guide clinical treatment. In our previous work, we introduced a finite element model of tissue expansion that predicted key patterns of strain and growth which were then confirmed by a porcine animal model also introduced by us. Here we use the data from a new set of experiments on the animal model to calibrate the computational model in a Bayesian framework. We find that growth can be explained based on the total deformation. The characteristic area growth rate is k e [0.01,0.022] [1/hrs]. Growth is anisotropic, the rostral-caudal axis shows greater deformation than the transverse axis, and the time scale of growth in the rostral-caudal direction is given by a rate parameters k1 e in the range [0.01,0.035 ] [1/hrs] compared to k2 e of [0.003,0.008] [1/hrs] in the transverse direction. Moreover, the results underscore the high variability in biological systems, and the need to create probabilistic computational models to predict tissue adaptation in realistic settings