A Recursive Bayesian Estimation Method for Measuring Kinetics of Amyloid Fibrillogenesis

Laura Kellman

I have long been fascinated by math, and more recently by biology. When my high school presented the opportunity to participate in research at a local university two years ago, I looked for a project that could help me see how the mathematics I learned in the classroom could be applied to help us better understand questions in biology . . . My advisor found Dr. David Eisenberg’s lab at the Molecular Biology Institute at UCLA, a lab studying, among other things, amyloid fibers and Alzheimer’s disease. I was introduced to Dr. James Stroud, who had developed a method applying Bayes’ theorem to data of amyloid fiber formation. With James’ help, I began working on writing code to create realistic simulations to test and refine the method. By testing the method and improving it where possible, we created a method that can be used to analyze real data . . When I began working in the lab, I knew next to nothing about Bayes’ theorem or amyloid fibers. Diving into the project meant learning things from an area of mathematics completely foreign to me, and simultaneously attempting to apply it to the real world. With a lot of help from my mentor, I came to understand and even contribute to the project.