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.
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References
Edward, J.S., Ibarra, R.U., Palsson, B.O.: In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotechnology 19, 125–130 (2001)
Karp, P.D., Paley, S.M., Krummenacker, M., Latendresse, M., Dale, J.M., Lee, T.J., Kaipa, P., Gilham, F., Spaulding, A., Popescu, L., Altman, T., Paulsen, I., Keseler, I.M., Caspi, R.: Pathway tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief Bioinform. 11(1), 40–79 (2010)
Mlecnik, B., Scheideler, M., Hackl, H., Hartler, J., Sanchez-Cabo, F., Trajanoski, Z.: PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways. Nucleic Acids Research 33(1), 633–637 (2005)
Wei, Z., Li, H.: A markov random field model for network-based analysis of genomic data. Bioinformatics 23(12), 1537–1544 (2007)
Sanguinetti, G., Noirel, J., Wright, P.C.: Mmg: a probabilistic tool to identify submodules of metabolic pathways. Bioinformatics 24(8), 1078–1084 (2008)
Varges, F.A., Pizzarro, F., Perez-Correa, J.R., Agosin, E.: Expanding a dynamic flux balance model of yeast fermentaion to genome-scale. BMC Systems Biology 5, 75 (2011)
Mo, M.L., Palsson, B.Ø., Herrgård, M.J.: Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Systems Biology 3, 37–41 (2009)
Priefert, H., Rabenhorst, J., Steinbüchel, A.: Biotechnological production of vanillin. Appl. Microbiol. Biotechnol. 6, 296–314 (2001)
Hancock, T., Takigawa, I., Mamitsuka, H.: Mining metabolic pathways through gene expression. Gene Expression 26(17), 2128–2135 (2010)
Hancock, T., Mamitsuka, H.: A Markov Classification Model for Metabolic Pathways. In: Salzberg, S.L., Warnow, T. (eds.) WABI 2009. LNCS, vol. 5724, pp. 121–132. Springer, Heidelberg (2009)
Reed, J.L., Palsson, B.O.: Thirteen Years of Building Constraint-Based InSilico Models of Escherichia coli. J. Bacteriol. 185(9), 2692–2699 (2003)
Brochado, A.R., Matos, C., Moller, B.L., Hansen, J., Mortensen, U.H., Patil, K.R.: Improved vanillin production in baker’s yeast through in silico design. Microbial Cell Factories 9, 84 (2010)
Boer, V.M., Crutchfield, C.A., Bradley, P.H., Botstein, D., Rabinowitz, J.D.: Growth-limiting intracellular metabolites in yeast growing under diverse nutrient limitations. Mol. Biol. Cell 21(1), 198–211 (2010)
Orth, J.D., Thiele, I., Palsson, B.Ø.: What is flux balance analysis? Nature Computational Biology 28, 245–248 (2010)
Kim, J., Reed, J.: OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Bioinformatics 4(53), 1–19 (2010)
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Salleh, A.H.M., Mohamad, M.S., Deris, S., Illias, R.M. (2013). Identifying Minimal Genomes and Essential Genes in Metabolic Model Using Flux Balance Analysis. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_43
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DOI: https://doi.org/10.1007/978-3-642-36546-1_43
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