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
Department, Program, or Center
Thomas H. Gosnell School of Life Sciences (COS)
Gary R. Skuse
Clark, K Jeselle, "Simulating Pathway-Based Steady States to Prevent Epithelial- Mesenchymal Transition in Ovarian Cancer" (2019). Thesis. Rochester Institute of Technology. Accessed from
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