Advances in computation and high-throughput methods of biomedical data collection have created tremendous opportunities to advance our understanding of visual physiology. We will test the hypothesis that genes and their protein products expressed in ocular tissues can be analyzed in silico using statistical and machine learning techniques and create knowledge regarding ocular gene regulation, structure and function. In particular, these studies will produce testable predictions that may be validated by bench experiments. A primary goal will be to characterize the regulatory elements that govern gene expression in ocular cells. In addition, these tools will be used to characterize ocular protein structure, particularly structure/function relationships and to make predictions about secondary and tertiary structure. Finally, in silico methods will be used to analyze the gene/protein networks that carry out cellular functions such as metabolism, signal transduction and regulation of gone expression. The following specific questions will be addressed in this research: [unreadable] [unreadable] 1) Can computational and statistical methods be used to identify DNA sequences that regulate [unreadable] ocular gene expression? Can these sequences be used to identify new, uncharacterized [unreadable] genes in the human genome? [unreadable] [unreadable] 2) Can these methods be used to predict protein structural features in both health and disease [unreadable] states? [unreadable] [unreadable] 3) Can gene/protein functional networks be elucidated in silico by the use of advanced [unreadable] analytical and modeling techniques? [unreadable] [unreadable] This proposal describes a four year training program that aims to develop the independent research abilities of the PI, and to prepare for a career as an individual investigator in computational biology and translational medicine. [unreadable] [unreadable]