Date of Award


Level of Access

Campus-Only Dissertation

Degree Name

Doctor of Philosophy (PhD)


Biomedical Sciences


Matthew A. Hibbs

Second Committee Member

Gary A. Churchill

Third Committee Member

Keith W. Hutchison


Pluripotent stem cell research is an active, often controversial field focused on a special type of cell that exists in vivo only during the earliest stages of embryonic development. These unique cells have the ability to self-renew to maintain their stem cell population or differentiate into cells with the potential to become any cell type in the developing embryo. By investigating pluripotent stem cells in vitro, researchers gain insights into biological processes, pathways, and interactions that influence early development, and contribute to our ever-evolving understanding of cellular reprogramming, tissue regeneration, degenerative diseases, and cancers. Although intensively studied for decades, the molecular mechanisms that dictate pluripotent stem cell fate are diverse and remain only partially understood.

The objective of this study is to clarify the molecular foundations of mouse and human pluripotent stem self-renewal by identifying novel genes and interactions that influence selfrenewal and contrasting self-renewal processes across species. For this work, I use a straightforward and flexible Bayesian network machine learning methodology for genomic data integration to generate consensus networks among protein-coding genes. To minimize confounding factors, I focus on one type of pluripotent stem cell, embryonic stem cells (ESCs), in two species, mouse and human. This study provides intriguing insights into novel genes involved in mouse and human ESC self-renewal, highlights important differences in developmental signaling and metabolic pathways that support ESC self-renewal in these species, and serves as the foundation for future pluripotent stem cell and cancer stem-like cell studies. To make the results of this study accessible to the research community, I provide a comprehensive set of computational resources (available online at that includes a powerful, dynamic network visualization tool designed to enable biologist end-users to explore my dense, complex biological networks. These resources may be used by stem cell researchers to discover novel regulators of embryonic stem cell self-renewal, test new hypotheses, and prioritize genes of interest for further study.