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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.


Subject
Date Range
2010 2020


The human transcriptional regulatory machine utilizes hundreds of transcription factors which bind to specific genic sites resulting in either activation or repression of targeted genes. Networks comprised of nodes and edges can be constructed to model the relationships of regulators and their targets. Within these biological networks small enriched structural patterns containing at least three nodes can be identified as potential building blocks from which a network is organized. A first iteration computational pipeline was designed to generate a disease specific gene regulatory network for motif detection using established computational tools. The first goal was to identify motifs that can …

Contributors
Striker, Shawn Scott, Plaisier, Christopher, Brafman, David, et al.
Created Date
2020

In most diploid cells, autosomal genes are equally expressed from the paternal and maternal alleles resulting in biallelic expression. However, as an exception, there exists a small number of genes that show a pattern of monoallelic or biased-allele expression based on the allele’s parent-of-origin. This phenomenon is termed genomic imprinting and is an evolutionary paradox. The best explanation for imprinting is David Haig's kinship theory, which hypothesizes that monoallelic gene expression is largely the result of evolutionary conflict between males and females over maternal involvement in their offspring. One previous RNAseq study has investigated the presence of parent-of-origin effects, or …

Contributors
Underwood, Avery, Wilson, Melissa, Buetow, Kenneth, et al.
Created Date
2019

Cancer is a disease which can affect all animals across the tree of life. Certain species have undergone natural selection to reduce or prevent cancer. Mechanisms to block cancer may include, among others, a species possessing additional paralogues of tumor suppressor genes, or decreasing the number of oncogenes within their genome. To understand cancer prevention patterns across species, I developed a bioinformatic pipeline to identify copies of 545 known tumor suppressor genes and oncogenes across 63 species of mammals. I used phylogenetic regressions to test for associations between cancer gene copy numbers and a species’ life history. I found a …

Contributors
Schneider-Utaka, Aika Kunigunda, Maley, Carlo C, Wilson, Melissa A, et al.
Created Date
2019

Fusion proteins that specifically interact with biochemical marks on chromosomes represent a new class of synthetic transcriptional regulators that decode cell state information rather than deoxyribose nucleic acid (DNA) sequences. In multicellular organisms, information relevant to cell state, tissue identity, and oncogenesis is often encoded as biochemical modifications of histones, which are bound to DNA in eukaryotic nuclei and regulate gene expression states. In 2011, Haynes et al. showed that a synthetic regulator called the Polycomb chromatin Transcription Factor (PcTF), a fusion protein that binds methylated histones, reactivated an artificially-silenced luciferase reporter gene. These synthetic transcription activators are derived from …

Contributors
Vargas, Daniel A., Haynes, Karmella, Wang, Xiao, et al.
Created Date
2019

This project was completed to understand the evolution of the ability to digest wood in termite symbiotic protists. Lower termites harbor bacterial and protist symbionts which are essential to the termite ability to use wood as a nutritional source, producing glycoside hydrolases to break down the polysaccharides found in lignocellulose. Yet, only a few molecular studies have been done to confirm the protist species responsible for particular enzymes. By mining publicly available and newly generated genomic and transcriptomic data, including three transcriptomes from isolated protist cells, I identify over 200 new glycoside hydrolase sequences and compute the phylogenies of eight …

Contributors
Sanderlin, Viola, Gile, Gillian H, Wojciechowski, Martin, et al.
Created Date
2019

There is an ongoing debate around the extent that anthropogenic processes influence both plant species distribution dynamics and plant biodiversity patterns. Past human food use may leave a strong legacy on not only the extent that food plants are dispersed and fill their potential geographic ranges, but also on food plant species richness in areas that have been densely populated by humans through time. The persistent legacy of plant domestication on contemporary species composition has been suggested to be significant in some regions. However, little is known about the effects that past human food use has had on the biogeography …

Contributors
Flower, Carolyn, Blonder, Benjamin, Hodgson, Wendy, et al.
Created Date
2019

Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnesses the target information available in the limited labeled data, as well as the information in the abundant unlabeled data to build strong predictive models. However, not all the included information is useful. For example, some features may correspond to noise and including them will hurt the predictive model performance. Additionally, some instances may not be as relevant to …

Contributors
Gaw, Nathan, Li, Jing, Wu, Teresa, et al.
Created Date
2019

The WNT signaling pathway plays numerous roles in development and maintenance of adult homeostasis. In concordance with it’s numerous roles, dysfunction of WNT signaling leads to a variety of human diseases ranging from developmental disorders to cancer. WNT signaling is composed of a family of 19 WNT soluble secreted glycoproteins, which are evolutionarily conserved across all phyla of the animal kingdom. WNT ligands interact most commonly with a family of receptors known as frizzled (FZ) receptors, composed of 10 independent genes. Specific interactions between WNT proteins and FZ receptors are not well characterized and are known to be promiscuous, Traditionally …

Contributors
Cutts, Joshua Patrick, Brafman, David A, Stabenfeldt, Sarah, et al.
Created Date
2019

The highly specialized telomerase ribonucleoprotein enzyme is composed minimally of telomerase reverse transcriptase (TERT) and telomerase RNA (TR) for catalytic activity. Telomerase is an RNA-dependent DNA polymerase that syntheizes DNA repeats at chromosome ends to maintain genome stability. While TERT is highly conserved among various groups of species, the TR subunit exhibits remarkable divergence in primary sequence, length, secondary structure and biogenesis, making TR identification extremely challenging even among closely related groups of organisms. A unique computational approach combined with in vitro telomerase activity reconstitution studies was used to identify 83 novel TRs from 10 animal kingdom phyla spanning 18 …

Contributors
Logeswaran, Dhenugen, Chen, Julian J-L, Ghirlanda, Giovanna, et al.
Created Date
2019

Understanding intratumor heterogeneity and their driver genes is critical to designing personalized treatments and improving clinical outcomes of cancers. Such investigations require accurate delineation of the subclonal composition of a tumor, which to date can only be reliably inferred from deep-sequencing data (>300x depth). The resulting algorithm from the work presented here, incorporates an adaptive error model into statistical decomposition of mixed populations, which corrects the mean-variance dependency of sequencing data at the subclonal level and enables accurate subclonal discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer simulations and real-world data, this new method, named model-based …

Contributors
Ahmadinejad, Navid, Liu, Li, Maley, Carlo, et al.
Created Date
2019