PhD Candidate, Department of Physics, Wake Forest University.
George P. Williams, Jr. Lecture Hall, (Olin 101)
Wed. Mar. 14, 2018, at 4:00 PM
There will be a reception with refreshments at 3:30 PM in the lounge. All interested persons are cordially invited to attend.
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.
This thesis work has been mentored by Professor Fred Salsbury. The PhD thesis defense will take place on March 19, 2018.