Mahdiar Sadeghi’s PhD Dissertation Defense

“Model-based decision making in life sciences”

Abstract:

Mathematical models are key tools in rational decision-making processes. A “good” model is expected to reproduce experimental observations, which enables predictions outside the previous experimental settings. The accuracy of predictions depends on the assumptions used to model the system. The objective of this study is to explore possible approaches to deploy models in order to generate new hypotheses in life sciences. A few biological systems relevant to protein translation, chemotherapy, immunotherapy, and epidemics are considered. Models are analyzed numerically/analytically to optimize a new decision/control. In protein translation processes, it is discovered that no switching policy is better than constant rates to maximize ribosome flow. In a particular experimental setting of chemotherapy, a new dosing plan for chemotherapy is identified and predicted to result in maximum shrinkage of the tumor volume. In immunotherapy, key features of binding kinetics of T-cell engagers in pre-clinical experiments are discussed. Moreover, epidemic models under social distancing guidelines are studied. Considering a single-interval social distancing based on the start time and the duration of the social distancing shows a surprising linear relationship. Some of the results presented in this dissertation are shown to be valid in multiple applications.

Committee:

Prof. Eduardo Sontag (Advisor)
Dr. Irina Kareva
Prof. Carey Rappaport
Prof. Bahram Shafai
Prof. Mark Niedre