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

Authors:
Abdul Hakim Mohamed Salleh;Mohd Saberi Mohamad;Safaai Deris;Rosli Md. Illias
Affiliations:
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia;Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia;Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia;Department of Bioprocess Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, UTM, Skudai, Johor, Malaysia
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Year:
2013

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Abstract

With the advancement in metabolic engineering technologies, reconstruction the genome of a host organism to achieve desired phenotypes for example, 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. Different 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 genome scale metabolic model of Saccharomyces Cerevisiae for L-phenylalanine production. The result has shown the reliability of this approach to find essential genes for metabolites productions, reduce genome size and identify production pathway that can further optimize the production yield and can be applied in solving other genetic engineering problems.