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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


The prevalence of obesity and obesity-related disorders have increased world-wide. In the last decade, the intestinal microbiome has become a major indicator of metabolic and gastrointestinal health. Previous research has shown that high-fat diet (HFD) consumption can alter the microbial composition of the gut by increasing the abundance of gram-positive bacteria associated with the onset of obesity and type 2 diabetes. Although, the most common form of obesity and metabolic syndrome intervention is exercise and diet, these recommendations may not improve severe cases of obesity. Thus, an important relevance of my project was to investigate whether the intake of an …

Contributors
Crawford, Meli'sa Shaunte, Sweazea, Karen L, Deviche, Pierre, et al.
Created Date
2019

This dissertation investigates the condition of skeletal muscle insulin resistance using bioinformatics and computational biology approaches. Drawing from several studies and numerous data sources, I have attempted to uncover molecular mechanisms at multiple levels. From the detailed atomistic simulations of a single protein, to datamining approaches applied at the systems biology level, I provide new targets to explore for the research community. Furthermore I present a new online web resource that unifies various bioinformatics databases to enable discovery of relevant features in 3D protein structures. Dissertation/Thesis

Contributors
Mielke, Clinton, Mandarino, Lawrence, Labaer, Joshua, et al.
Created Date
2013