Ovarian cancer is a complex disease that involves gene regulatory dysfunction and that requires a systemic viewpoint to fully understand. Applying executable biology to ovarian cancer research and leveraging documented regulatory protein interactions, one can efficiently inform the prediction of characteristic gene-product activation using a logical model checking approach. Using this innovative approach to reducing terms and satisfying constraints, this thesis presents a strategy for applying regulatory systems biology to cancer research. By viewing ovarian cancer pathways like an electrical circuit, and constructing a pathway model with natural language processing tools, gene product expression patterns that have not been explained by traditional wet-bench biology are able to be predicted in silico. This research yields seven gene products whose perturbation is predicted to be sufficient to induce the epithelial-mesenchymal transition of ovarian cancer.

Publication Date


Document Type


Student Type


Degree Name

Bioinformatics (MS)

Department, Program, or Center

Thomas H. Gosnell School of Life Sciences (COS)


Gary R. Skuse

Advisor/Committee Member

Gordon Broderick

Advisor/Committee Member

Matthew Morris


This thesis has been embargoed. The full-text will be available on or around 10/30/2019.


RIT – Main Campus

Available for download on Sunday, October 27, 2019