In Silico Prediction of Metabolic Gene Expression

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DESCRIPTION (provided by applicant): Today there exists a clear need for the development of in silico models capable of integrating with high throughput experimental technologies. This need has risen due to the inherent complexity of biological systems and the enormity of currently generated biological data sets such as those from whole-genome transcriptional profiling. We have played a leading role in developing methods for computational systems biology through the construction of genome -scale in silico models of prokaryotes. These metabolic models are based on the principles of constraints-based modeling. As one of the next stages in the logical development and implementation of in silico models, the focus of this STTR program is aimed at assessing the ability to use constraints-based models of metabolism to provide integrative analysis of metabolic gene expression patterns under varying environmental conditions. In addition to advancing the application range of in silico modeling and simulation, success of this program will lead to an improvement in gene expression analysis, namely the introduction of modeling-assisted interpretation of whole-genome expression patterns. This will have an impact on all life sciences research that is utilizing gene expression profiling technologies to address human health and disease. In this Phase II STTR effort we aim to accelerate the development and commercialization of the modeling technology developed in Phase I. The most valuable commercial aspect of this project lies in the development of a modeling approach that can be coupled to expression array analysis for any organism. Herein we have selected baker's yeast, Saccaromyces cerevisiae, as the showcase organism for a proof-of-concept This technology development wiII be embodied in an expanded version of our proprietary modeling platform, SimPheny (registered trademark of Genomatica), to contain a module dedicated to the management, visualization, and integration of gene expression data with in silico models. In addition we will also generate a model for baker's yeast, Saccharomyces cerevisiae, which is of clear commercial value. Yeast strains are some of the most important and widely used microorganism in academic and industrial life sciences research. All together we are building toward an integrated computational/experimental platform for to drive a more efficient biological discovery. It is anticipated that in silico predictive biology will become an indispensable tool for the life sciences industry in the near future.