Date/Time
Date(s) - 12/05/2022
3:00 pm - 4:00 pm
Location
Communicore, C1-009
Grace O’Connell is an Associate Professor in the Department of Mechanical Engineering at the University of California, Berkeley. She is the co-director of the Berkeley Biomechanics Laboratory, and her research interests are in soft tissue mechanobiology and tissue engineering. O’Connell received PhD in Bioengineering from the University of Pennsylvania in 2009, where her research focused on intervertebral disc biomechanics with age, degeneration, and injury. O’Connell’s research group employs computational modeling and experimental approaches to study the effect of aging and disease on tissue- and joint-level mechanobiology. She has received many awards including the 2019 YC Fung Young Investigator Award and NSF CAREER Award, and was inducted into the AIMBE College of Fellows in 2021. She is also the Associate Dean for Inclusive Excellence for the College of Engineering.
ABSTRACT
Finite element models provide a valuable tool for studying disease progression, risk of tissue failure, or repair strategies. To date, many models for biological tissues employ hyperelastic material descriptions with material properties that have no direct physical interpretation. This seminar will focus on development, validation, and application of a multi-scale structure-based model developed for the intervertebral disc.
The disc is a fiber-reinforced composite structure. Model development was initiated by calibrating model parameters to mechanical behavior at the sub-tissue scale. Fiber bundles and non-fibrous material were modeled as separate materials using triphasic mixture theory, allowing for direct physical interpretation of the material properties. The resulting parameters were used to create tissue- and joint-level models of the disc and the model-predicted mechanical behavior was compared to experimental data in the literature for model validation. Lastly, the model was uses to assess the impact of complex loading on the relative risk of tissue failure. Specifically, the model was used to predict the risk of disc herniation. Findings from this work highlight significant challenges in replicating clinically relevant disc herniation using commonly applied experimental techniques.