Title: An Integrated Computational Pipeline to Construct Patient-Specific Cancer Models

Abstract: Precision oncology largely involves tumor genomics to guide therapy protocols. Yet, it is well known that many commonly mutated genes cannot be easily targeted. Even when genes can be targeted, resistance to therapy is quite common. A rising field with promising results is that of mathematical biology, where \textit{in silico} models are often used for the discovery of general principles and novel hypotheses that can guide the development of new treatments. A major goal is that eventually \textit{in silico} models will accurately predict clinically relevant endpoints and find optimal control interventions to stop (or reverse) disease progression. Thus, it is vital to develop an ecosystem of researchers to optimize the model creation and analysis pipeline. The modeling pipeline posited within this dissertation (dubbed the Synergistic Model Acquisition and Target Analysis (\textit{SMATA}) pipeline) includes equation learning (to develop topological network communications and functions), attractor analysis, application of phenotype control theory, and simulation of suggested targets. The results herein help provide a proof of concept in the path towards personalized medicine through a means of mathematical systems biology. As such, we apply these strategies to one of the most detrimental cancers, Pancreatic Ductal Adenocarcinoma (PDAC). While any cancer diagnosis is life-altering, pancreatic cancer is among the most discouraging to receive because of its extreme difficulty to overcome. Using our pipeline, we were able to corroborate previous publications and even make some new promising discoveries.