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.

Library of Congress Subject Headings

Ovaries--Cancer--Treatment--Computer simulation; Systems biology

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


RIT – Main Campus