In Silico Prediction of Metabolic Gene Expression Patter

生物学研究的各个层次的研究人员现在可以获得的生物信息最近爆炸，部分原因是高通量技术对基因和蛋白质进行测序，并确定其表达模式，试图捕捉生物功能的算法复杂性。总的来说，这些数据已经开始使细胞作为生命系统的研究成为可能，并允许开发计算机模型来描述细胞系统的系统特性和功能性能，特别是代谢。这些计算机模拟模型基于通量平衡分析和代谢途径分析的完善原理。该提案旨在评估使用计算机模拟代谢模型预测不同环境条件下代谢基因表达模式的能力。特别是酿酒酵母的模型将被构建和实施，以产生在各种模拟条件下的生物体的代谢行为和基因表达模式的预测。然后将这些预测与通过使用微阵列技术进行全基因组转录谱分析产生的实验结果进行比较。我们打算证明使用计算机模型定量预测代谢表型和定性预测基因表达模式的技术可行性。拟议的商业应用：除了推进计算机建模和模拟的应用范围之外，该提议的成功将导致基因表达分析的重大改进，即引入全基因组表达模式的建模辅助解释。

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.