Computational Platforms and Methods for Capturing Systems-Level Protein Dynamics Across Space and Time

Abstract

The advent and continued advancement of technologies that can quantify the levels of different biomolecules (DNA, RNA, protein, etc.) have granted researchers the ability to gain global insights into the cellular regulation of biological pathways. However, the increasing scale, breadth, and number of these “omics” studies presents a challenge for data interpretation and the extraction of biologically relevant information. In addition to providing measurements for thousands of targets, oftentimes, these studies span multiple different experimental conditions and/or time points. As a result, a challenge of modern computational biology lies in the analysis, visualization, and integration of these multidimensional datasets across space and time. The work presented within this dissertation seeks to address the above challenge by developing methods and tools that facilitate these tasks in the context of proteomics data and applies these approaches to characterize how protein dynamics change between healthy and diseased states. Specifically, it focuses on developing tools and methodologies to help discover how changes in protein localizations (Chapter II), protein-protein interactions (Chapter III), and protein abundances (Chapter IV) contribute to cellular pathologies.