Application of mathematical tools for metabolic design of microbial ethanol production

Many attempts to engineer cellular metabolism have failed due to the complexity of cellular functions. Mathematical and computational methods are needed that can organize the available experimental information, and provide insight and guidance for successful metabolic engineering. Two such methods are reviewed here. Both methods employ a (log)linear kinetic model of metabolism that is constructed based on enzyme kinetics characteristics. The first method allows the description of the dynamic responses of metabolic systems subject to spatiotemporal variations in their parameters. The second method considers the product-oriented, constrained optimization of metabolic reaction networks using mixed-integer linear programming methods. The optimization framework is used in order to identify the combinations of the metabolic characteristics of the glycolytic enzymes from yeast and bacteria that will maximize ethanol production. The methods are also applied to the design of microbial ethanol production metabolism. The results of the calculations are in qualitative agreement with experimental data presented here. Experiments and calculations suggest that, in resting Escherichia coli cells, ethanol production and glucose uptake rates can be increased by 30% and 20%, respectively, by overexpression of a deregulated pyruvate kinase, while increase in phosphofructokinase expression levels has no effect on ethanol production and glucose uptake rates.