Identifying minimal genomes and essential genes in metabolic model using flux balance analysis

Abstract

With the advancement in metabolic engineering technologies, recon-struction the genome of a host organism to achieve desired phenotypes for ex-ample, to optimize the production of metabolites can be made. However, due to the complexity and size of the genome scale metabolic network, significant components tend to be invisible. This research utilizes Flux Balance Analysis (FBA) to search the essential genes and obtain minimal functional genome. Dif-ferent from traditional approaches, we identify essential genes by using single gene deletions and then we identify the significant pathway for the metabolite production using gene expression data. The experiment is conducted using ge-nome scale metabolic model of Saccharomyces Cerevisiae for L-phenylalanine production. The result has shown the reliability of this approach to find essen-tial genes for metabolites productions, reduce genome size and identify produc-tion pathway that can further optimize the production yield and can be applied in solving other genetic engineering problems.