In recent years, there have been many advancements in interdisciplinary work that utilizes both computer science and its principles. One such area lies at the intersection of biology and computer science and is known as \"bioinformatics\" or \"computational biology\". In the early days of bioinformatics, this took the form of de novo sequence assembly or extensive biological databases. Now, bioinformatics has expanded into a full-fledged subdiscipline of computer science that seeks to gather, store, analyze, and distribute biological data. Computational biology methods have enabled scientists to ask questions that would have been unanswerable prior to the rise of high-performance computing. My thesis explores the application of high-performance computing and parallelizable algorithms to protein folding and dynamics. Advancements in high-performance computing have allowed systems with millions of atoms to be simulated. Pairwise force calculations between particles can comprise up to 90% of simulation time, and introducing parallelizable algorithms that can be run on multiple GPUs allows us to cut down on computation time significantly. My research serves to explore the behavior of the full-length wildtype p53 protein and its isoforms in order to learn about their potential regulatory effects by utilizing high-performance computing techniques.
"},{"label":"Degree","value":["Bachelor of Arts with Departmental Honors"]},{"label":"Author-supplied Keywords","value":["Computational biology","Molecular dynamics","High-performance computing"]},{"label":"Administrative Note","value":"Drupal Node ID: 24255