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As a central regulatory hub, protein kinase A (PKA) coordinates numerous biological processes, including discrete effects on metabolic enzymes all the way to global effects on organism growth and development. Given the diversity of roles PKA can play in normal physiology it is not surprising that dysregulation of PKA leads to diverse pathophysiology and disease. While PKA is one of the most studied kinases, its role in diseases has remained largely unknown. With this dissertation, I aim to map the contributions of the Gαs-PKA pathway in disease from multiple perspectives. First, I aim to unify the view of PKA-driven pathophysiology by utilizing mutational data from hereditary diseases and cancer databases to define a family of diseases known as the Gαs-PKA pathway signalopathies. Further I utilize evidence from the scientific literature to bring functional understanding to these diverse disease states. Second, I leverage our genetic understanding of PKA pathway mutations to develop several transgenic mouse models of cancer, providing evidence of the role of GNAS in cancer initiation and tools for application in multiple tissue contexts. Finally, I take advantage of affinity purification mass spectrometry (AP-MS) approaches to identify novel PKA interactors. I uncover several functional themes and ultimately focus on specific RNA binding proteins interactions that mediate new mechanisms of posttranscriptional regulation. In total, this dissertation makes important progress towards the understanding of PKA-driven disease, providing a framework for the advancement of the field and ultimately unique opportunities for identifying therapeutic interventions.

Proteins are such a vital piece of the scientific community's understanding of molecular interactions in organisms, which is why the accurate analysis of proteins are such a pivotal piece of drug and toxin design. In this research we introduce the foundation of a software suite of interconnected novel tools that use machine learning and deep learning tools. These tools will provide not only insights and connected patterns about the proteins that they study, but also a new data set of information that can be used in future research. This tool would enable analysis from several different inputs, such as: the primary protein structure, FASTA, DSSP, or PDB files. Also, in this research we introduce a database of results that would use similar analysis techniques as social media analysis tools to attempt to analyze the interconnected nature of proteins in a meaningful way. This research is not intended to be considered a final product but a foundation and proof of concept. The favorable results found by the initial tools being used as inspiration along with a clear path of the future to build a more complete tool with the goals of one day being made publicly so the information can grow to its full potential.

Identifying causal genes underlying susceptibility to human disease is a problem of primary importance in post-genomic era and current biomedical research. Recently, there has been a paradigm shift of such gene-discovery efforts from rare, monogenic conditions to common "oligogenic" or "multifactorial" conditions such as asthma, diabetes, cancers and neurological disorders. These conditions are referred as multifactorial because, susceptibility to these diseases is attributed to the combinatorial effects of genetic variation at a number of different genes and their interaction with relevant environmental exposures. The expectation is that identification and characterization of the causal genes implicated in the inherited component of disease susceptibility will lead to substantial advances in our understanding of disease. These advances in turn can lead to improvements in diagnostic accuracy, prognostic precision, the range and targeting of available therapeutic options and ultimately realize the promise of personalized or "tailor-made" medicine. The objective of my thesis therefore is to design, develop, and validate computational approaches for identification and prioritization of these causal genes. The first approach tests the hypothesis that the majority of genes that impact or cause disease share membership in any of several functional relationships. We use a p-value-based meta-analysis method to prioritize the candidate genes based on functional annotation. For the very first time, we use and demonstrate, the utility of mouse phenotype annotations in human disease gene prioritization. Since this approach is limited to only genes with functional annotation, and because many human genes are yet to be functionally classified, we have developed another approach that is independent of gene functional annotations. We implemented a set of new algorithms to prioritize genes based on protein-protein interaction networks. Large scale cross-validation were performed for comparison and evaluation of the methods, and to determine the associated parameters. Our results demonstrate that the functional annotation-based method performs better than other approaches. Although the performance of the network-based method was not as good as functional annotation-based method, it is much simpler to implement, apply, and execute. The best performance was however achieved, as demonstrated through asthma test case, by combining the results from the two methods.