Date Approved

2026

Degree Type

Open Access Senior Honors Thesis

Department or School

Chemistry

First Advisor

Steven K. Backues, Ph.D.

Second Advisor

Hedeel Evans, Ph.D

Third Advisor

Harriet Lindsay, Ph.D.

Abstract

Autophagy is a key cellular recycling process that helps cells maintain homeostasis and survive stress by breaking down damaged organelles and misfolded proteins. When autophagy is disrupted, it contributes to diseases such as cancer, Parkinson’s disease, and Alzheimer’s disease, so getting accurate measurements of autophagosome size and number is important for understanding how this pathway works. In this project, we are working on improving a computational model in CompuCell3D that simulates how autophagic bodies clump inside the yeast vacuole, which we later compare to real transmission electron microscopy images. We generated starting files with defined body numbers and sizes, simulated how they clump together, and analyzed the output using Python code that measures data such as aspect ratio, circularity, coziness, and compactness. To improve the speed and accuracy of the simulations, we have been testing different Neighbor Order values and implementing a new “Global Forces” approach that uses distance-dependent force vectors to pull bodies together faster. While this has improved convergence, some parameter settings caused unwanted “squaring” of the bodies; testing different variable ranges currently to maintain realistic shapes is a work in progress. Alongside the modeling, we have collected and analyzed about 800 transmission electron microscopy images of yeast vacuoles, using an AI-based segmentation tool to gather actual size and number distributions of autophagic bodies. At the same time, we produced strains carrying an HA-tagged ATG8 gene, which is expected to improve Western blot detection of the Atg8 protein, and we tested these strains by Western blotting. These combined computational and experimental results are helping us refine the simulation so that it more closely matches real biological data, which will ultimately make it easier to estimate autophagosome size and number more accurately.

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