Browse Theses

Inflammation and extracellular matrix (ECM) remodeling are important components regulating the response of the left ventricle (LV) to myocardial infarction (MI). Significant cellular and molecular level contributors can be identified by analyzing high throughput genomics and proteomics data. Such large scale data embedded important temporal and spatial information that need to be analyzed and interpreted using systems biology approaches, in order to integrate this information into dynamic models to predict and explain mechanisms of cardiac healing post-MI. The goal of this dissertation is to analyze the gene expressions post-MI systemically with three aims. Our aim one is to discover the biomarkers in a biological data set using an unsupervised data mining method. This aim is achieved by evaluating a biclustering algorithm using Sparse Singular Value Decomposition (SSVD) method to find the genes that have significant impact on left ventricular remodeling and compare the effects of different drugs post-MI. Aim two is to analyze temporal molecular expression profiles to identify biomarkers and obtain useful insights into biological processes. This task was implemented by introducing a novel temporal biclustering method that incorporates local short-term expression pattern into global temporal progression to find the most significant biomarkers. In Aim three, we will model the dynamics of interactions between the biomarkers by differential equations and estimate parameters in nonlinear mathematical models for biological pathways with a Bayesian algorithm.