In Silico Prediction of Metabolic Gene Expression Patter

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The recent explosion of biological information now available to researchers at all levels of biological investigation has resulted in-part from high-throughput technologies to sequence genes and proteins, and to determine their expression patterns, in an attempt to capture the algorithmic complexity of biological functions. Collectively, this data has begun to enable the study of cells as living systems, and has allowed for the development of in silico models to describe the systemic properties and functional performance of cellular systems, and metabolism in particular. These in silico models are based on well established principles of flux balance analysis and metabolic pathway analysis. This proposal is aimed at assessing the ability of using in silico models of metabolism to predict metabolic gene expression patterns under varying environmental conditions. In particular a model of Saccharomyces cerevisiae will be constructed and implemented to generate predictions on the metabolic behavior and gene expression patterns of the organism under various simulated conditions. These predictions will then be compared to experimental results generated through the use of microarray technology for whole-genome transcription profiling. We intend to demonstrate the technical feasibility of using in silico models to quantitatively predict metabolic phenotypes and qualitatively predict gene expression patterns. PROPOSED COMMERCIAL APPLICATIONS: In addition to advancing the application range of in silico modeling and simulation, success of this proposal will lead to a major improvement in gene expression analysis, namely the introduction of modeling- assisted interpretation of whole-genome expression patterns.