2014 MU dissertations - Access restricted to MU
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Item Data analysis and prediction of protein posttranslational modification(University of Missouri--Columbia, 2014) Yao, Qiuming; Xu, Dong, 1965-[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Protein posttranslational modification (PTM) occurs broadly after or during protein biosynthesis, to assist folding or activate function during the protein lifetime. Among all types of PTMs, protein phosphorylation is widely recognized as the most pervasive, enzyme-catalyzed post-translational modification in eukaryotes. In particular, plants have higher magnitude of this signaling mechanism in terms of the protein kinase frequency within the genome compared to other eukaryotes. Phosphorylation site mapping using high-resolution mass spectrometry has grown exponentially. In Arabidopsis alone there are thousands of experimentally-determined phosphorylation sites. Likewise, other types of post translational modification data are rapidly increasing too. Acetylation proteome is another big data set in PTM kingdom. To provide an easy access of these modification events in a user-intuitive format we have developed P3DB, The Plant Protein Phosphorylation Database (p3db.org). This database is a repository for plant protein phosphorylation site data. These data can be queried for a protein-of-interest using an integrated BLAST function to search for similar sequences with known phosphorylation sites among the multiple plants currently investigated. Thus, this resource can help identify functionally-conserved phosphorylation sites in plants using a multi-system approach. Centralized by these phosphorylation data, multiple related data and annotations are provided, including protein-protein interaction (PPI), gene ontology, protein tertiary structures, orthologous sequences, kinase/phosphatase classification and Kinase Client Assay (KiC Assay) data. P3DB thus is not only a repository, but also a context provider for studying phosphorylation events. In addition, P3DB incorporates multiple network viewers for the above features, such as PPI network, kinase-substrate network, phosphatase-substrate network, and domain co-occurrence network to help study phosphorylation from a systems point of view. Furthermore, P3DB reflects a community-based design through which users can share data sets and automate data depository processes for publication purposes. Since P3DB is a comprehensive, systematic, and interactive platform for phosphoproteomics research, many data analyses can be done based on it. For example, the disorder analysis and the sequence conservation can be done based on the P3DB datasets. Many researchers downloaded and did some meaningful analysis based on P3DB infrastructure. Although with the development of the high-resolution mass spectrometry protein phosphorylation sites can be reliably identified, the experimental approach is time-consuming and resource-dependent. Furthermore, it is unlikely that an experimental approach could catalog an entire phosphoproteome. Computational prediction of phosphorylation sites provides an efficient and flexible way to reveal potential phosphorylation sites, facilitate experimental phosphorylation site identification and provide hypotheses in experimental design. Musite is a powerful tool that we developed to predict phosphorylation sites based solely on protein sequence. Musite integrates data preprocessing, feature extraction, machine-learning method, and prediction models into one comprehensive tool. Musite (http://musite.net) can be extended to all types of post translational modification study, as long as the dataset contains sufficient modification sites. To further improve the performance of Musite, a generalized motif tree applying fuzzy logic is introduced to compensate the machine learning based prediction. On one hand, using a tree based approach and fuzzy variables help to interpret the final rules, in order to help biologists to obtain the significant patterns. On the other hand, its extracted rule sets essentially generalize the motifs and reveal more information. It can be paired with traditional classification method and provide better interpretation, pre-filtering and analyzing power. Comparing to traditional motif extraction, the fuzzy motif decision tree is able to borrow more information from the observations and thus it may extract more novel motifs or more comprehensive patterns. It can be applied on kinase specific phosphorylated peptides to achieve more insights of the phosphorylation events. A comprehensive database (P3DB), a well-developed prediction tool (Musite), and a generalized motif constructor (Fuzzy Motif Tree) combined enable researchers to investigate the phosphorylation and other posttranslational modification events more thoroughly and thus to reveal more underlying biological significance by applying these computational resources.Item Therapeutic targets for spinal muscular atrophy : Inhibiting the inhibitors(University of Missouri--Columbia, 2014) Osman, Erkan Y.; Lorson, Christian L.[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Spinal muscular atrophy (SMA) is an autosomal recessive disorder that is a leading genetic cause of infantile death. SMA is the most common inherited motor neuron disease and occurs in approximately 1: 6,000 live births. The gene responsible for SMA is called survival motor neuron-1 (SMN1). A human-specific copy gene is present on the same region of chromosome 5q called SMN2. SMN2 is nearly identical to SMN1; however, mutations in SMN2 have no clinical consequence if SMN1 is retained. The reason why SMN2 cannot prevent disease development in the absence of SMN1 is that the majority of SMN2-derived transcripts are alternatively spliced, resulting in a truncated and unstable protein. The presence of SMN2 in all SMA patients is fundamental to the biology of the disease; however, from a translational perspective, targeting SMN2 may prove to be the most important therapeutic opportunity for all patients. The presence of SMN2 opens the door to a number of exciting therapeutic strategies, including anti-sense oligonucleotides (ASOs) that prevent the pathogenic SMN2 splicing event. Our efforts are focused on several repressor regions upstream and downstream of SMN2 exon 7. Importantly, when manipulating these repressor regions, hallmarks of the disease at the cellular level such as neuromuscular junction pathology in various SMA animal models are corrected. Currently, there are no approved SMA-specific compounds, and developing a broad array of therapeutic strategies to address this complex disease is essential. The development and design of highly-potent ASOs provide novel molecular targets for SMA therapeutics that can dramatically improve disease phenotype and extend patients' life span.Item Development of a flow-based predictive model for the coalescence of fused deposition modeling filaments(University of Missouri--Columbia, 2014) Graybill, Brian; El-Gizawy, A. Sherif[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] As rapid prototyping processes continue to be developed, there is increasing use of such processes for the production of end-use parts. Fused deposition modeling (FDM) is a particularly favorable method for fabricating end-use parts because of the wide selection of materials available for the process such as Ultem 9085, prized by the aerospace industry for its high strength-to-weight ratio. To confidently employ FDM parts in service requires a thorough understanding of their behavior under expected loading conditions and the ability to predict their success for failure in a particular application. The strength of an FDM part is derived from the amount of bonding that occurs between the polymer filaments as they are deposited. Thus, an accurate prediction of this bond length should lead naturally to an accurate prediction of part strength. Models simulating the heat transfer and coalescence experienced by a pair of adjacent filaments are developed and presented. The models are executed across a range of build parameters to help determine flexibility, and provide a value for the predicted bond length. To validate the models, FDM parts are built from Ultem 9085, cross sectioned, and imaged. The images allow measurements of actual bond lengths to be obtained. The measured bond lengths are compared to the predicted bond lengths. Only a select number of bond lengths measurements are obtained because of variations in microstructure corresponding to various build parameters. A predictive accuracy of 95 % is desired, but the model is unable to achieve it due to estimates of critical data that is unavailable and the variability inherent in the FDM process. However, the simulations provide a significant foundation for future modeling efforts aimed at providing a model capable of predicting bond lengths, and therefore strengths, of FDM parts.Item Electronic health record system implementation processes at critical access hospitals(University of Missouri--Columbia, 2014) Craven, Catherine Kershaw; Sievert, Maryellen Cullinan[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The US government allocated $30 billion to implement electronic health records (EHRs) in hospitals and provider practices through policy addressing Meaningful Use (MU). Most small, rural hospitals, particularly those designated as Critical Access Hospitals (CAHs), comprising nearly a quarter of US hospitals, had not implemented EHRs before. Little is known about implementation in this setting. Socio-technical factors differ between larger hospitals and CAHs, which continue to lag behind other hospitals in EHR adoption. Qualitative methods employing Glaserian Grounded Theory were used to develop question protocols and conduct 69 interviews and eight focus groups onsite at four CAHs in Arkansas (1), Kansas (2), and Tennessee (1) where staff were undertaking EHR implementation. In addition, 41 phone interviews were conducted with a spectrum of implementation experts, including newly-minted peer-experts from 10 additional CAHs, who had completed EHR implementations. Twenty-eight themes emerged from coding and analysis. Key barriers and facilitators for EHR implementation at CAHs were identified, and a prospective implementation framework for hospitals for these and similar, small rural hospitals was developed, with additional recommendations for ehealth policy makers and other stakeholders.Item Bayesian analysis of capture-recapture model and diagnostic test in clinical trials(University of Missouri--Columbia, 2014) Zheng, Dan; He, Chong Z.[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Capture-recapture models have been widely used to estimate the size of a target wildlife population. There are three major sources of variations that can affect capture probabilities: time (i.e., capture probabilities vary with time or trapping occasion), behavioral response (i.e., capture probabilities vary due to a trap response of animals to the first capture), and heterogeneity (i.e., capture probabilities vary by individual animal). There are eight models regarding possible combinations of these factors, including M0, Mt, Mb, Mh, Mtb, Mth, Mbh, and Mtbh. A capture-recapture model (Mb model) was created to present the behavioral response effect. The objective Bayesian analysis for the population size was developed and compared with common maximum likelihood estimates (MLEs). Simulation results demonstrate the advantages of the objective Bayesian over MLEs. Two real examples about a deer mouse are presented and one R package (OBMbpkg) was built for application. Companion diagnostics (CDx) for personalized medicine is commonly applied to in vitro diagnostic (IVD) industry and clinical trials for specific disease or treatment with biomarkers (e.g. molecular targets). The Bayesian method with Gibbs sampler was used to estimate the potential bias caused by imperfect CDx under the targeted design, where only patients with a positive diagnosis were enrolled the clinical trials. A simulation study was conducted to evaluate the performance of the Bayesian method and to compare with the EM algorithm. The Bayesian model selection method with G-prior was used to test treatment effects of targeted drugs for patients with biomarkers under the targeted design. A simulation study was conducted to evaluate the performance of the Bayesian method and to compare it with the original method and EM method when sample size is small. Eventually a biomarker-stratified design was studied, while patients enrolled in clinical trials could be divided into two groups (i.e., those with a positive or negative diagnosis). Both the EM algorithm and Bayesian method were used to estimate the potential bias caused by imperfect CDx. Simulation results demonstrate the advantages of the Bayesian method over the original method and EM method.