Dr. Adrian Buganza-Tepole

Skin mechanobiology across scales


Skin, like most living tissue, adapts to mechanical cues, for example after wound healing, reconstructive surgery, or in tissue expansion. We have created computational models that combine mechanics and mechanobiology to describe the deformation, growth, and remodeling of skin, and applied these models to clinically relevant scenarios. Together with experiments on a porcine model, and leveraging ML tools such as multi-fidelity Gaussian processes, we have performed Bayesian inference to learn mechanistically how skin grows in response to stretch and heals after being wounded. One central aspect in creating these multi-scale computational models is the consideration of uncertainty in mechanics and biology of tissues. Deterministic models are simply not enough to make accurate predictions. Computational models, like experiments, need to account for the different sources of uncertainty in order to lead to trustworthy results that reflect the inherent variability of tissues.


Suggested readings:

Han T, et al. Bayesian calibration of a computational model of tissue expansion based on a porcine animal model. Acta Biomaterialia. 2022;137:136-46.

Tac V, Costabal FS, Tepole AB. Data-driven tissue mechanics with polyconvex neural ordinary differential equations. Comput Method Appl Mech Eng. 2022;398:115248.

Sohutskay DO, Tepole AB, Voytik-Harbin SL. Mechanobiological wound model for improved design and evaluation of collagen dermal replacement scaffolds. Acta Biomaterialia. 2021;135:368-82.

Lee T, Bilionis I, Tepole AB. Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression. Comput Method Appl Mech Eng. 2020;359:112724.