Theses and Dissertations
Date of Award
8-1-2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematics
First Advisor
Thoa Thieu
Second Advisor
Liet Vo
Third Advisor
Kristina Vatcheva
Abstract
The intracellular transport and exocytosis of insulin granules in pancreatic ?-cells are heavily influenced by the cytoskeleton. The microtubule (MT) network provides pathways for motor-driven transport while also imposing geometric constraints. To investigate this, we developed a computational framework combining stochastic geometric optimization and drift diffusion modeling. We modeled the cytoplasm as a 3D domain with randomly distributed MTs, using optimization to quantify void structures. Comparing deterministic and stochastic optimization revealed that stochastic methods better characterize crowded MT environments, preventing premature convergence and accurately matching experimental conditions. Using this optimized network, we simulated granule dynamics via stochastic differential equations encompassing Brownian motion, active transport, and steric interactions. Our simulations demonstrate that while dense cytoskeletal structures severely restrict passive vesicle movement, active motor-driven transport significantly improves mobility and membrane targeting. Ultimately, this framework offers a robust, quantitative approach for studying vesicle transport dynamics within crowded intracellular spaces.
Recommended Citation
Rios, X. S. (2026). Stochastic Modeling of Insulin Granule Transport in Microtubule-Dense Cytoplasm Highlights the Importance of Active Transport in Overcoming Steric Caging [Master's thesis, The University of Texas Rio Grande Valley]. ScholarWorks @ UTRGV. https://scholarworks.utrgv.edu/etd/2062
Comments
Copyright 2026 Xavier Rios. All Rights Reserved. https://proquest.com/docview/3409025104