Public Presentation in Olin 107.
Mon. Mar. 19, 2018, at 2:00 PM.
Freddie R. Salsbury, Jr., Advisor.
The defense will follow.
Applying statistical and machine learning, I have addressed key issues in the field of computational biophysics. The guiding principle in this work has been removing bias and conveying uncertainty. To that end, I have contributed numerous methods for interpreting biopolymer ensemble data without the need for prior knowledge or setting of biasing parameters. Additionally, in all of these works, I have provided a careful discussion of the limits of these methods and how researchers might visually convey the inherent uncertainty, including displaying what are effectively error bars on biopolymer structures. I have worked to remove bias even in estimating the amount of sampling needed for any time-dependent multi-dimensional process. These contributions may move the field forward in its ability to remove bias and convey uncertainty in statistically rigorous ways.
After introducing these methods, I proceed with applications of them to the study of a chemotherapeutic nucleic acid called F10 – a 10mer of 5-fluoro-2′-deoxyuridine-5′-O-monophosphate. Here I uncover the mechanism for a previously observed interaction with zinc and magnesium, leading to a general investigation of F10’s interactions with metal ions. I conclude by proposing a stabilizing chemical perturbation to the polymer and discussing implications for drug delivery.