Integration of Microarray Data into a Genome-Scale Metabolic Model to Study Flux Distribution after Gene Knockout
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Integration of Microarray Data into a Genome-Scale Metabolic Model to Study Flux Distribution after Gene Knockout

Authors: Mona Heydari, Ehsan Motamedian, Seyed Abbas Shojaosadati

Abstract:

Prediction of perturbations after genetic manipulation (especially gene knockout) is one of the important challenges in systems biology. In this paper, a new algorithm is introduced that integrates microarray data into the metabolic model. The algorithm was used to study the change in the cell phenotype after knockout of Gss gene in Escherichia coli BW25113. Algorithm implementation indicated that gene deletion resulted in more activation of the metabolic network. Growth yield was more and less regulating gene were identified for mutant in comparison with the wild-type strain.

Keywords: Metabolic network, gene knockout, flux balance analysis, microarray data, integration.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125483

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References:


[1] K. Raman and N. Chandra, “Flux balance analysis of biological systems: Applications and challenges,” Briefings in Bioinformatics, vol. 10, no. 4, pp. 435-449, 2009.
[2] F. Llaneras and J. Picó, “Stoichiometric modeling of cell metabolism,” Journal of Bioscience and Bioengineering, vol. 105, no. 1, pp. 1-11, 2008.
[3] J. S. Edwards, M. Covert, and B. Palsson, “Metabolic modeling of microbes: The flux-balance approach,” Environmental Microbiology, vol. 4, no. 3, pp. 133-140, 2002.
[4] A. M. Feist and B. O. Palsson, “The biomass objective function,” Current Opinion in Microbiology, vol. 13, no. 3, pp. 344-349, 2010.
[5] K. J. Kauffman, P. Prakash, and J. S. Edwards, “Advances in flux balance analysis,” Current Opinion in Biotechnology, vol. 14, no. 5, pp. 491-496, 2003.
[6] A. Varma and B. O. Palsson, “Metabolic flux balancing: Basic concepts, scientific and practical use,” Nature Biotechnology, vol. 12, no. 10, pp. 994-998, 1994.
[7] J. S. Edwards and B. O. Palsson, “The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 10, pp. 5528-5533, 2000.
[8] J. S. Edwards, R. U. Ibarra, and B. O. Palsson, “In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data,” Nature Biotechnology, vol. 19, no. 2, pp. 125-130, 2001.
[9] I. Family, J. Forster, J. Nielsen, and B. O. Palsson, “Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 23, pp. 13134-13139, 2003.
[10] J. Forster, I. Famili, B. O. Palsson, and J. Nielsen, “Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae,” OMICS A Journal of Integrative Biology, vol. 7, no. 2, pp. 193-202, 2003.
[11] R. U. Ibarra, J. S. Edwards, and B. O. Palsson, “Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth,” Nature, vol. 420, no. 6912, pp. 186-189, 2002.
[12] K. Kiviharju, U. Moilanen, M. Leisola, and T. Eerikäinen, “A chemostat study of Streptomyces peucetius var. caesius N47,” Applied Microbiology and Biotechnology, vol. 73, no. 6, pp. 1267-1274, 2007.
[13] A. M. Feist, J. C. M. Scholten, B. Ø. Palsson, F. J. Brockman, and T. Ideker, “Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri,” Molecular Systems Biology, vol. 2, pp. 2006.
[14] V. Satish Kumar, J. G. Ferry, and C. D. Maranas, “Metabolic reconstruction of the archaeon methanogen Methanosarcina Acetivorans,” BMC Systems Biology, vol. 5, pp. 2011.
[15] O. Gonzalez, T. Oberwinkler, L. Mansueto, F. Pfeiffer, E. Mendoza, R. Zimmer, and D. Oesterhelt, “Characterization of growth and metabolism of the haloalkaliphile Natronomonas pharaonis,” PLoS Computational Biology, vol. 6, no. 6, pp. 1-10, 2010.
[16] C. B. Milne, P. J. Kim, J. A. Eddy, and N. D. Price, “Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology,” Biotechnology Journal, vol. 4, no. 12, pp. 1653-1670, 2009.
[17] J. L. Hjersted, M. A. Henson, and R. Mahadevan, “Genome-scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture,” Biotechnology and Bioengineering, vol. 97, no. 5, pp. 1190-1204, 2007.
[18] Q. Zhao and H. Kurata, “Genetic modification of flux for flux prediction of mutants,” Bioinformatics, vol. 25, no. 13, pp. 1702-1708, 2009.
[19] D. Segre, D. Vitkup, and G. M. Church, “Analysis of optimality in natural and perturbed metabolic networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 23, pp. 15112-15117, 2002.
[20] T. Shlomi, O. Berkman, and E. Ruppin, “Regulatory on/off minimization of metabolic flux changes after genetic perturbations,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 21, pp. 7695-7700, 2005.
[21] M. J. Herrgard, S. S. Fong, and B. O. Palsson, “Identification of genome-scale metabolic network models using experimentally measured flux profiles,” PLoS Computational Biology, vol. 2, no. 7, pp. 0676- 0686, 2006.
[22] J. Ihmels, R. Levy, and N. Barkai, “Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae,” Nature Biotechnology, vol. 22, no. 1, pp. 86-92, 2004.
[23] J. D. Orth, T. M. Conrad, J. Na, J. A. Lerman, H. Nam, A. M. Feist and B. O. Palsson, “A comprehensive genome-scale reconstruction of Escherichia coli metabolism--2011,” Mol Syst Biol, vol. 7, pp. 535, 2011.
[24] A. R. Joyce, J. L. Reed, A. White, R. Edwards, A. Osterman, T. Baba, H. Mori, S. A. Lesely, B. Ø. Palsson, and S. Agarwalla, “Experimental and computational assessment of conditionally essential genes in Escherichia coli,” Journal of Bacteriology, vol. 188, no. 23, pp. 8259- 8271, 2006.
[25] S. A. Nizam and K. Shimizu, “Effects of arcA and arcB genes knockout on the metabolism in Escherichia coli under anaerobic and microaerobic conditions,” Biochemical Engineering Journal, vol. 42, no. 3, pp. 229- 236, 2008.
[26] M. K. Chattopadhyay, W. Chen, and H. Tabor, “Escherichia coli glutathionylspermidine synthetase/amidase: phylogeny and effect on regulation of gene expression,” FEMS Microbiol Lett, vol. 338, no. 2, pp. 132-40, 2013.