Data-driven skin biophysics

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October 27, 2022

1:30pm – 2:30pm

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BIODS 260 presents:
Dr. Adrian Buganza-Tepole
10/27/22
130 pm-2:30 pm
Location: MSOB x303

Dr. Adrian Buganza-Tepole is an Associate Professor of Mechanical Engineering and Biomedical Engineering (courtesy) at Purdue University. He obtained his Ph.D. in Mechanical Engineering from Stanford University in 2015 and was a postdoctoral fellow at Harvard University for a year before joining Purdue as a faculty member in 2016. He was also a Miller Visiting Professor at UC Berkeley during Spring 2022. His group studies the interplay between mechanics and mechanobiology of skin. Using computational simulation, machine learning, and experimentation, his group seeks to characterize the multi-scale mechanics of skin to understand the fundamental mechanisms of this tissue’s mechano-adaptation in order to improve clinical diagnostics and interventional tools.

Title: Data-driven skin biophysics  

Abstract: 

The recent explosion in machine learning (ML) and artificial intelligence (AI) algorithms has started a revolution in many engineering fields, including computational biophysics. This talk focuses on our recent efforts to leverage ML methods to increase our fundamental understanding of skin and its unique ability to adapt to mechanical cues. The first project that will be described is skin growth in tissue expansion, a popular reconstructive surgery technique that grows new skin in response to sustained supra-physiological loading. We have created computational models that combine mechanics and mechanobiology to describe the deformation and growth of expanded skin. 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. The second half of the talk will explore how mechanical cues can be key drivers of wound healing pathologies such as fibrosis. I will show computational models of reconstructive surgery and wound healing for a murine model of wound healing and in patient-specific cases. Once again, ML methods enable new kinds of analyses such as optimization under uncertainty and inverse parameter calibration which are not achieved with traditional approaches.

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.

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