Location

Krone EBB - Children's Healthcare of Atlanta Seminar Room
Faculty/Staff Public Graduate students

Jimmy L. Ding
BioE PhD Defense Presentation
Date and Time: Monday, May 1st, 2023, at 10:30 AM

Location: Krone EBB - Children's Healthcare of Atlanta Seminar Room

Zoom Link: https://gatech.zoom.us/j/2320107482?pwd=SzVQSzVmTGhKNTMxSWF4by9yM1U1QT09

 

 

Advisor:
Hang Lu, PhD (Georgia Institute of Technology)

Committee:

Brandon Dixon, PhD (Georgia Institute of Technology)

Patrick McGrath, PhD (Georgia Institute of Technology)
Simon Sponberg, PhD (Georgia Institute of Technology)

Lena Ting, PhD (Georgia Institute of Technology)

 

 

Microfluidic and Computational Tools to Characterize Changes in Mechanosensory Functon Throughout Aging in Caenorhabditis Elegans

      Mechanosensation is the basis for touch, hearing, and balance. It plays a vital role in how we navigate and operate in the world. Our capability for mechanosensation deteriorates as we age, but there is currently a lack of understanding of the mechanisms driving this change. Caenorhabditis elegans are a microscopic nematode with a simple nervous system used as a model organism to study both aging and neural circuits. Previous work has identified and characterized the components of the mechanosensory circuit and how neuronal morphology changes with age. However, the effect of aging on the mechanosensory function is unknown and requires characterization. To do so requires novel tools that allow for delivery of controlled and consistent stimuli to animals of differing phenotype.

      In this thesis, I developed new computational and microfluidic-based tools to address the limitations in characterizing perturbations to mechanosensory function such as aging-related changes to enable better understanding of this crucial sensory modality. I engineer a microfluidic pipeline that allows for measurement of subtle changes in neuronal function and utilize it to characterize age-related changes in mechanosensory neuronal function. I then develop a computational model to determine the effect of age-related changes to biomechanical properties on delivery of mechanical stimuli. Finally, I use reverse correlation to characterize the temporal dynamics of mechanosensory neurons and their robustness to age-related changes in C. elegans. These tools provide deeper understanding and new opportunities for studying mechanosensation.