MechE PhD Prospectus Defense: Quan Nguyen

  • Starts: 12:30 pm on Friday, September 6, 2024
  • Ends: 2:30 pm on Friday, September 6, 2024
TITLE: EXPERIMENT-BASED COMPUTATIONAL FRAMEWORK TO UNDERSTAND THE ROLE OF MECHANICS ON 3D TISSUE ASSEMBLY AND REPAIR

ABSTRACT: Mammalian cells have an incredible ability to assemble and repair tissue and organs. Though it is well known that mechanical forces play a critical role in this process, the mechanisms by which forces regulate new tissue formation and organization remain poorly understood. Motivated by this gap in our knowledge, we seek to unravel the role of mechanical boundary conditions in the process of tissue assembly and repair. This thesis contains (1) an open-source software to segment mechanically heterogeneous domains, (2) an image analysis pipeline to segment and track microtissues during the repair process, (3) a random fiber network mechanical model to represent microtissue contraction, and (4) future work combining the extracted data, experimental tools, and computational models available to create a framework that captures the effects of mechanical boundary conditions on the tissue repair process. The first section contains an open-source software to identify the homogeneous subdomains within a mechanically heterogeneous domain using unsupervised learning. Due to the lack of ground truth in heterogeneous materials, we developed a dataset generation pipeline to provide in-silico data with known ground truth. Then, we leveraged ensemble clustering to find the mechanically homogeneous subdomains. The second section covers our current work on developing an image analysis pipeline to segment and track the microtissue repair process. Here, we extract the mechanical properties of the experimental setup via segmentation, and obtain the kinematics of the microtissue via tracking. This work will allow us to obtain the necessary mechanical features for our microtissue repair model in future work. In the third section, we modeled the microtissue undergoing contraction immediately after wounding with a mechanical random fiber network model. We will also obtain the mechanical properties of the microtissue (e.g., fiber alignment) post-contraction to predict the repair process in future work. Finally, we will explore different modeling options with the mechanical information from the previous sections to understand the tissue repair process, and test our computational model against unseen experiments to ensure the generalizability of our model. With this work, we hope to provide a robust framework combining computational modeling with an experimental validation and testing process.

COMMITTEE: ADVISOR/CHAIR Professor Emma Lejeune, ME; Professor Katherine Yanhang Zhang, ME/BME/MSE; Professor Paul Barbone, ME/MSE

Location:
EMA 205, 730 Commonwealth Ave