The purpose of this study was to conduct a national survey to examine job satisfaction of community college academic deans as measured by the Minnesota Satisfaction Questionnaire (MSQ) and an Individual Data Sheet (IDS) and to determine if academic deans will pursue the community college presidency in meeting the current leadership crisis. This study assessed the relationship of selected personal characteristics, unit-related characteristics, facets of job satisfaction, and career aspirations of academic deans. Six research questions directed this study. Four hundred community college academic deans were randomly assigned as participants and represented all 50 states. The usable response rate from the 400 participants was 50.5% (n=202) representing all 50 states. Demographic data pertaining to gender, age, ethnicity, degree status, tenure in position, gross annual salary, number of hours worked per week, major responsibilities, size of college, location of college, number of full-time and part-time faculty supervised, number of full-time and part-time staff supervised, and career aspirations were collected through use of the IDS. The 1977 Long-Form MSQ was used to measure general, intrinsic, and extrinsic job satisfaction. Appropriate summary statistics, correlations, and regressions were computed to answer all six-research questions. Community college academic deans were neither dissatisfied nor satisfied with an MSQ sample mean score of 3.828. The findings indicated that 55.5% (n=112) were neither dissatisfied nor satisfied. Only 76 academic deans or 37.5% stated that they were satisfied and three deans or 1.5% were very satisfied. Ten deans or 5% reported being dissatisfied and one or 0.5% dean reported being very dissatisfied. Only 15% or 30 deans reported that they had career aspirations to pursue the community college presidency within the next one to ten years. The results also indicated that those academic deans that do not desire to be a community college president are slightly more satisfied than those deans who want to be a president. The results of the survey indicate that academic deans with the lowest job satisfaction score desired to move along the academic leadership pathway, and the deans that were more satisfied wanted to move in another direction or stay a dean.

This thesis presents Pathway Informed Analysis (PIA), a classification method for predicting disease states (diagnosis) from metabolic profile measurements that incorporates biological knowledge in the form of metabolic pathways. A metabolic pathway describes a set of chemical reactions that perform a specific biological function. A significant amount of biological knowledge produced by efforts to identify and understand these pathways is formalized in readily accessible databases such as the Kyoto Encyclopedia of Genes and Genomes. PIA uses metabolic pathways to identify relationships among the metabolite concentrations that are measured by a metabolic profile. Specifically, PIA assumes that the class-conditional metabolite concentrations (diseased vs. healthy, respectively) follow multivariate normal distributions. It further assumes that conditional independence statements about these distributions derived from the pathways relate the concentrations of the metabolites to each other. The two assumptions allow for a natural representation of the class-conditional distributions using a type of probabilistic graphical model called a Gaussian Markov Random Field. PIA efficiently estimates the parameters defining these distributions from example patients to produce a classifier. It classifies an undiagnosed patient by evaluating both models to determine the most probable class given their metabolic profile. We apply PIA to a data set of cancer patients to diagnose those with a muscle wasting disease called cachexia. Standard machine learning algorithms such as Naive Bayes, Tree-augmented Naive Bayes, Support Vector Machines and C4.5 are used to evaluate the performance of PIA. The overall classification accuracy of PIA is better than these algorithms on this data set but the difference is not statistically significant. We also apply PIA to several other classification tasks. Some involve predicting various manipulations of the metabolic processes performed in experiments with worms. Other tasks are to classify pigs according to properties of their dietary intake. The accuracy of PIA at these tasks is not significantly better than the standard algorithms.

To effectively understand and treat complex diseases such as cancer, mathematical and statistical modeling is essential if one wants to represent and characterize the interactions among the different regulatory components that govern the underlying decision making process. Like in any other complex decision making networks, the regulatory power is not evenly distributed among its individual members, but rather concentrated in a few high power "commanders". In biology, such commanders are usually called masters or canalizing genes. Characterizing and detecting such genes are thus highly valuable for the treatment of cancer. Chapter II is devoted to this task, where we present a Bayesian framework to model pathway interactions and then study the behavior of master genes and canalizing genes. We also propose a hypothesis testing procedure to detect a "cut" in pathways, which is useful for discerning drugs' therapeutic effect. In Chapter III, we shift our focus to the understanding of the mechanisms of action (MOA) of cancer drugs. For a new drug, the correct understanding of its MOA is a key step for its application to cancer treatments. Using the Green Fluorescent Protein technology, researchers have been able to track various reporter genes from the same cell population for an extended period of time. Such dynamic gene expression data forms the basis for drug similarity comparisons. In Chapter III, we design an algorithm that can identify mechanistic similarities in drug responses, which leads to the characterization of their respective MOAs. Finally, in the course of drug MOA study, we observe that cells in a hypothetical homogeneous population do not respond to drug treatments in a uniform and synchronous way. Instead, each cell makes a large shift in its gene expression level independently and asynchronously from the others. Hence, to systematically study such behavior, we propose a mathematical model that describes the gene expression dynamics for a population of cells after drug treatments. The application of this model to dose response data proviodes us new insights of the dosing effects. Furthermore, the model is capable of generating useful hypotheses for future experimental design.

Glypican-3 (GPC3) belongs to a family of cell surface proteoglycans. GPC3 regulates the activity of several morphogens and growth factors that play critical roles during development. Disrupting the function of GPC3 leads to disease, including the overgrowth disease Simpson Golabi Behmel Syndrome (SGBS) and Cancer. Previous work has shown that GPC3 is over expressed in Hepatocellular Carcinoma (HCC), and that HCC proliferation is stimulated through GPC3 mediated activation of the Wnt signaling pathway. Glypicans are known to regulate Wnt signaling in a variety of model organisms including Drosophila and mouse. This work investigates the hypothesis that GPC3 stimulates Wnt signaling by facilitating/stabilizing the interaction between Wnt and its receptor Frizzled (Fzd). Consistent with this hypothesis, we found that GPC3 is able to bind both Wnt and Fzd. The binding of GPC3 to Fzd is mediated by the GPC3 glycosaminoglycan chains and by the cysteine rich domain of Fzd.

The Sonic hedgehog (Shh) signaling pathway plays a critical role in murine gastric development. When Shh is knocked out in the mouse embryonic stomach, glandular epithelial hyperplasia occurs. Furthermore, this phenotype was mimicked in Gli3−/−, but not Gli2−/− stomachs. I utilized three additional mouse models that modulate Gli3 activity to better understand the role of Gli3 in the developing stomach - the Gli3Δ699/Δ699 ,Gli3P1−4/P1−4, and Kif7−/− mice. The Gli3P1−4/P1−4 stomach displayed glandular epithelial overgrowth, as did the Kif7−/− stomach to a lesser extent; the Gli3Δ699/Δ699 stomach displayed glandular hypoplasia. Moreover, the Gli3P1−4/P1−4 and Kif7−/− stomachs have a thicker circular smooth muscle, and the Gli3Δ699/Δ699 had a thinner one relative to wild-type. It appears that altering the balance of Gli3 in favour of its activator results in gastric glandular epithelial and circular smooth muscle hyperplasia, and a balance favouring the Gli3 repressor results in hypoplasia.

The shikimate pathway links carbohydrate metabolism to biosynthesis of the aromatic amino acids in plants, fungi, bacteria and apicomplexan parasites. The pathway has seven enzymatic steps which convert erythrose-4-phosphate and phosphoenolpyruvate to chorismate, the precursor of tyrosine, tryptophan and phenylalanine. Due to the absence of the pathway in mammalian species, the enzymes are attractive targets for herbicides and antimicrobials. Shikimate dehydrogenase (SDH) catalyses the fourth step, the NADP-dependent reversible reduction of 3-dehydroshikimate to shikimate. Five SDH homologs – AroE, Ael1, YdiB, RifI and SdhL – have been identified through kinetic analysis and phylogenetic studies in the bacterium Pseudomonas putida. SDH homolog gene knockouts (KO) were used to characterize their functions. The AroE KO and Ael1 KO were successfully constructed via gene SOEing of the SDH homolog with a gentamycin antibiotic cassette and homologous recombination via electroporation into WT P. putida KT2440. Preliminary characterization tested KO growth, auxotroph recovery and fluorescent activity.

Glypican-3 (GPC3) belongs to a family of cell surface proteoglycans. GPC3 regulates the activity of several morphogens and growth factors that play critical roles during development. Disrupting the function of GPC3 leads to disease, including the overgrowth disease Simpson Golabi Behmel Syndrome (SGBS) and Cancer. Previous work has shown that GPC3 is over expressed in Hepatocellular Carcinoma (HCC), and that HCC proliferation is stimulated through GPC3 mediated activation of the Wnt signaling pathway. Glypicans are known to regulate Wnt signaling in a variety of model organisms including Drosophila and mouse. This work investigates the hypothesis that GPC3 stimulates Wnt signaling by facilitating/stabilizing the interaction between Wnt and its receptor Frizzled (Fzd). Consistent with this hypothesis, we found that GPC3 is able to bind both Wnt and Fzd. The binding of GPC3 to Fzd is mediated by the GPC3 glycosaminoglycan chains and by the cysteine rich domain of Fzd.

The Sonic hedgehog (Shh) signaling pathway plays a critical role in murine gastric development. When Shh is knocked out in the mouse embryonic stomach, glandular epithelial hyperplasia occurs. Furthermore, this phenotype was mimicked in Gli3−/−, but not Gli2−/− stomachs. I utilized three additional mouse models that modulate Gli3 activity to better understand the role of Gli3 in the developing stomach - the Gli3Δ699/Δ699 ,Gli3P1−4/P1−4, and Kif7−/− mice. The Gli3P1−4/P1−4 stomach displayed glandular epithelial overgrowth, as did the Kif7−/− stomach to a lesser extent; the Gli3Δ699/Δ699 stomach displayed glandular hypoplasia. Moreover, the Gli3P1−4/P1−4 and Kif7−/− stomachs have a thicker circular smooth muscle, and the Gli3Δ699/Δ699 had a thinner one relative to wild-type. It appears that altering the balance of Gli3 in favour of its activator results in gastric glandular epithelial and circular smooth muscle hyperplasia, and a balance favouring the Gli3 repressor results in hypoplasia.

KCN-1 is known to reduce tumor growth 6-fold in mice implanted with LN229 glioma cells. Although this inhibitor is effective, the mechanism of action for KCN-1 is not well understood. Based on preliminary studies, KCN-1 reduces tumor growth by disrupting the HIF 1 (hypoxia-induced factor-1) pathway. The binding target of KCN-1 needs to be investigated in order to develop KCN-1 or its analogs for therapeutic applications. In this research, a molecule was designed and synthesized for the identification of the binding target of KCN-1. Specifically, this molecule contains the inhibitor (KCN-1), a photocleavable linker, beads, and the affinity label (L DOPA). When UV light shines on the linker, the trans-alkene isomerizes to cis-alkene and undergoes intramolecular ring-closing reaction, which helps cleave the immobilized bead from the linker. The immobilized bead is used to separate the binding fragment attached to the photocleavable linker from the solution after enzyme digestion. The affinity label (L-DOPA) reacts with a nucleophile from the binding target and creates a covalent bond. If the design is successful, this method is able to analyze the mass of the peptide sequence and determine the binding target of KCN-1.

This thesis examines the retention of best practices by clinicians after the implementation of an integrated care pathway for patients with congestive heart failure. While the literature suggests there are many reasons why the implementation of best practices is difficult, there is little information on the sustainability of best practices once implemented.<p>Using a qualitative research design guided by Rogers theory of Diffusion of Innovations the researcher interviewed seven clinicians who participated in the implementation of the pathway. A thematic analysis revealed several themes that ran throughout participants responses. <p>While the participants indicated they see value in best practices, they also identified barriers to getting that knowledge into practice and keeping it there. A spectrum of factors, including individual autonomy, time, resources, organizational support and the organization of the system all played a role.<p>In the end, participants revealed that although small pieces of the pathway remain in practice, the pathway itself is no longer used by clinicians to manage patients with congestive heart failure.