Date of Award
5-12-2024
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical Engineering and Computer Science
Advisor(s)
Chilukuri Mohan
Second Advisor
Shikha Nangia
Subject Categories
Computer Sciences | Physical Sciences and Mathematics
Abstract
Understanding membrane protein assembly using computational methods is critical in developing drug delivery strategies for tight junction diseases such as Alzheimer’s and Crohn’s diseases. While experimental methods, such as freeze-fracture micrographs, provide microscopic details of the protein strand networks, they cannot offer molecular-level information, which is necessary for drug design. This work attempts to close the gap by computationally predicting strand networks of the claudin family of proteins that form tight junctions across neighboring cells in epithelial and endothelial tissues. Using claudin dimer data collected by the Protein AssociatioN Energy Landscape (PANEL) method coupled with some Markovian approximations, we built a linear time algorithm to generate claudin networks of over 1 million proteins, which has not been attempted before. Representing such detailed biological systems in silico required a systematic application of probability theory and design principles in programming. This work will allow us to bridge micron and angstrom length scales and provide molecular-level details of the claudin strand assembly. This study can also be applied to problems in other domains that require assembly of entities given the probabilities of events.
Access
Open Access
Recommended Citation
Shukla, Advait, "STOCHASTIC ALGORITHMS FOR SIMULATING PROTEIN ASSEMBLY IN BIOLOGICAL CELLS" (2024). Theses - ALL. 837.
https://surface.syr.edu/thesis/837
