In silico identification of gene amplification targets based on analysis of production and growth coupling

Abstract

Genome-scale metabolic models (GEMs) can be utilized to better understand the genotype-phenotype relationship in microbial metabolism. Manipulation strategies based on analysis of metabolic flux distributions using constraint-based methods have been validated to be effective for designing strains. Herein, we first investigated the coupled relationship of growth and production, and subsequently proposed an algorithm, called analysis of production and growth coupling (APGC), to identify amplification targets for improving production of the desired metabolite. The logical transformation of the genome-scale metabolic models (LTM) could enable a gene-level prediction, that is, direct gene targets would be determined through APGC. This algorithm was successfully employed to simulate heterogeneous biosynthesis of the antioxidant lycopene in Escherichia coli, and target genes for the improvement of lycopene production were identified. These identified gene targets were unambiguous and were closely related to the supply of essential precursors and cofactors for lycopene production, and most of these have been validated as effective in enhancing the yield of lycopene.