Development and Application of Metabolic Network Models


In recent years, computer aided redesigning methods based on genome-scale metabolic network models (GEMs) have become a popular tool in the design of genetically engineered strains and helped biologists to decipher metabolism. Most of these methods, however, are hindered by intractable computing times, such as the prediction of high level knockout strategies for the overproduction of desired metabolite targets. In this study, a framework called IdealKnock has been developed to efficiently break through the limitation of maximum knockout number in reasonable time and suggest knockout strategies with better performance. Moreover, the complex gene-reaction relationships in most GEMs have resulted in limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. We have therefore proposed a couple of methods to simplify the gene-reaction associations by introducing intermediate pseudo reactions or pseudo gene controlling reactions, making it possible to generate genetic design and allowing more metabolic engineering applications such as direct integration of expression data into GEMs.