Genome minimization method based on metabolic network analysis and its application to Escherichia coli

Abstract:

The minimum life is one of the most important research topics in synthetic biology. Minimizing a genome while at the same time maintaining an optimal growth of the cells is one of the important research objectives in metabolic engineering. Here we propose a genome minimization method based on genome scale metabolic network analysis. The metabolic network is minimized by first deleting the zero flux reactions from flux variability analysis, and then by repeatedly calculating the optimal growth rates after combinatorial deletion of the non-essential genes in the reduced network. We applied this method to the classic E. coli metabolic network model ---iAF1260 and successfully reduced the number of genes in the model from 1 260 to 312 while maintaining the optimal growth rate unaffected. We also analyzed the metabolic pathways in the network with the minimized number of genes. The results provide some guidance for the design of wet experiments to obtain an E. coli minimal genome.