Evaluating And Improving Stoichiometrically Constrained Models Of Yeast Metabolism For Application To Design Of Metabolic Engineering Strategies

Abstract

This document presents the results of efforts to apply the Yeast Consensus Reconstruction model of the Saccharomyces cerevisiae metabolic network to develop a metabolic engineering strategy for industrial strain improvement. Following a review of the development of mathematical models of metabolism, it describes an evaluation of the Consensus Reconstruction. We find that the computational reconstruction of this portion of metabolism differs from the biochemistry of this pathway as described in the literature. Our efforts to correct these discrepancies are described in Chapter 4. The updated model improves both the accuracy of the metabolic reconstruction and the prediction of viability and auxotrophy phenotypes, thus demonstrating that literature-based curation is a technique which can be successfully applied to improve the model. Chapter 5 describes an in silico screen for formate-producing yeast mutants. By working to reproduce an in silico screen previously conducted using the iND750 model, we found that the computational prediction of formate-producing yeast mutants is sensitive to implementation details and reaction constraints when using either the iND750 or the Yeast model. Our results suggest that comparative analysis of constraint based models is a useful tool for improving models of the yeast metabolic network. The main text concludes with a summary and discussion of future research opportunities in Chapter 6. MATLAB scripts which enable evaluation of model predictive accuracy and demonstrate model applications such growth simulation and mutant library screening are included as appendices. This work contributes to the ongoing effort to develop systems biotechnology tools which will enable the rational design of new microbial strains, and which may enable broader industrial-scale application of biotechnology. The fields of computational biology and systems biotechnology are young, and abundant opportunity remains to develop and apply this technology to meet human needs.