Advisor:
Dr. May D. Wang, BME

Thesis Committee:
Dr. Melissa Kemp, BME
Dr. Dong M. Shin, Emory
Dr. Brani Vidakovic, BME
Dr. Howard Weiss , MATH

The rapid advancement of large-scale and high-throughput experimental technologies has enabled the study of biology from a systems perspective. However, the analysis of the large datasets generated by these technologies for biological insight remains a challenging task. The proposed thesis research develops, evaluates and applies mathematical and computational approaches to analyze and model biological systems using large-scale biological data. First, statistical models are used to develop similarity measures with a probabilistic interpretation. The focus of this aim is to develop an analytical technique for imaging mass spectrometry and gene expression microarray datasets. Second, the performances of alternative methods for exploratory data analysis are evaluated and compared for different biological systems. The goal of this aim is to examine whether there exist patterns that capture biological system characteristics in the performances of different analytical approaches. Third, these findings are applied to large-scale datasets to establish a dynamic model of cancer progression and therapeutic response. The purpose of this aim is to use large-scale data analysis to link sub-cellular system dynamics with higher-level spatiotemporal features of disease progression. These aims contribute to ongoing efforts in bioinformatics and computational biology to develop in silico approaches that provide insight into biological systems.