Home WakeSpace Scholarship › Electronic Theses and Dissertations

GPU-Optimized Graph Theory Analysis of Allosteric Protein Signaling Networks

Electronic Theses and Dissertations

Item Files

Item Details

title
GPU-Optimized Graph Theory Analysis of Allosteric Protein Signaling Networks
author
Stevens, Cody Alexander
abstract
Graph algorithms have been widely applied to science and engineering problems, but the practical applications of graph algorithms to every-growing, large data sets requires significant computational time, motivating the need for efficient, parallel solutions on modern computing architectures. Here, we develop a novel GPU-optimized parallel algorithm for measuring the betweenness centrality of edges of a graph that is over 1000x faster than the CPU implementation of the algorithm in the NetworkX software package for 1,000-5,000 nodes. Inspired by biochemical mutagenesis experiments, we also introduce a new approach to measure how the betweenness centrality changes with the perturbation of a graph. As a direct application, we performed MD simulations of several protein-RNA complex, where we abstracted the residues and their interactions as nodes and edges, respectively, to characterize the molecular interaction network. We then systematically compare the change in the betweenness upon the deletion of the residue, akin to mutagenesis experiments performed in the laboratory, to create a predictor for mutagenesis experiments. Finally, we compared communal detection results to previously determined protein domains, finding a high correlation between residue-residue interactome communities and larger protein domains.
subject
Betweenness
GPU
Graph Theory
Parallel
Perturbation
contributor
Cho, Samuel S (committee chair)
Torgersen, Todd (committee member)
Turkett, William H (committee member)
date
2015-06-23T08:36:02Z (accessioned)
2015-06-23T08:36:02Z (available)
2015 (issued)
degree
Computer Science (discipline)
identifier
http://hdl.handle.net/10339/57193 (uri)
language
en (iso)
publisher
Wake Forest University
type
Thesis

Usage Statistics