Dr. Adrian Buganza-Tepole

Skin mechanobiology across scales

ABSTRACT:

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.