Title: Metabolites production improvement by identifying minimal genomes and essential genes using flux balance analysis

Authors: Abdul Hakim Mohamed Salleh; Mohd. Saberi Mohamad; Safaai Deris; Rosli Md. Illias

Addresses: Artificial Intelligence and Bioinformatics Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia ' Artificial Intelligence and Bioinformatics Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia ' Artificial Intelligence and Bioinformatics Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia ' Department of Bioprocess Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia

Abstract: With the advancement in metabolic engineering technologies, reconstruction of the genome of host organisms to achieve desired phenotypes can be made. However, due to the complexity and size of the genome scale metabolic network, significant components tend to be invisible. We proposed an approach to improve metabolite production that consists of two steps. First, we find the essential genes and identify the minimal genome by a single gene deletion process using Flux Balance Analysis (FBA) and second by identifying the significant pathway for the metabolite production using gene expression data. A genome scale model of Saccharomyces cerevisiae for production of vanillin and acetate is used to test this approach. The result has shown the reliability of this approach to find essential genes, reduce genome size and identify production pathway that can further optimise the production yield. The identified genes and pathways can be extendable to other applications especially in strain optimisation.

Keywords: bioinformatics; metabolites production; metabolic engineering; metabolic networks; minimal genome; essential genes; flux balance analysis; metabolic reconstruction; gene expression data; significant pathways; Saccharomyces cerevisiae; vanillin; acetate.

DOI: 10.1504/IJDMB.2015.068955

International Journal of Data Mining and Bioinformatics, 2015 Vol.12 No.1, pp.85 - 99

Received: 21 Feb 2013
Accepted: 19 Jul 2013

Published online: 22 Apr 2015 *

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