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In silico Evaluation of the Effect of Pfl Gene Knockout on the Production of D-lactate by Escherichia coli Genome Scale Model Using the Optflux Software Platform


  • Bioinformatics Research Group (BIRG), Biosciences & Health Sciences Department, Universiti Teknologi Malaysia, Skudai 81310 Johor Bahru, Malaysia


The increase availability of genome scale metabolic models of Escherichia coli and computational successes is revolutionizing the field of metabolic engineering and synthetic microbiology. E. coli has been experimentally established to produce D-lactate under micro-aerobic conditions when pyruvate formate lyase (PFL) genes are knocked out. However, investigation on the in silico prediction and for evaluation of the effect of PFL genes knockout on the production of D-lactate using E. coli genome scale metabolic model with regulatory on/off minimization (ROOM) under the OptFlux software platform remained under explored. Here, we demonstrate that metabolic engineering strategies using the OptFlux software platform by gene knockout simulation of pflA/b0902, pflB/b0903, pflC/b3952 and pflD/b3951 have been predicted to increase D-lactate production in E. coli and hence maintaining a growth rate that is 96% of the wild-type model. The deletion of the PFL genes have been established to increase D-lactate production in E. coli. The results obtained in this study is in agreement with the previously established experimental studies. These findings suggests that the OptFlux software platform using ROOM as the simulation algorithm, can prospectively and effectively predict future metabolic engineering targets for increased D-lactate production in E. coli and/or other microbial chemical syntheses.


D-Lactate, Escherichia coli Model, Gene Knockout Simulation, Metabolic Engineering, Optflux Software.

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  • Zhu J, Shimizu K. The effect of pfl gene knockout on the metabolism for optically pure D-lactate production by Escherichia coli. Appl Microbiol Biotechnol. 2004; 64(3):367–75.
  • Okano K, Tanaka T, Ogino C, Fukuda H, Kondo A. Biotechnological production of enantiomeric pure lactic acid from renewable resources: recent achievements, perspectives, and limits. Appl Microbiol Biotechnol. 2010; 85(3):413–23.
  • Mazumdar S, Clomburg JM, Gonzalez R. Escherichia coli strains engineered for homofermentative production of D-lactic acid from glycerol. Appl Environ Microbiol. 2010; 76(13):4327–36.
  • Zhou S, Shanmugam KT, Ingram LO. Functional Replacement of the Escherichia coliD-(-)-Lactate Dehydrogenase Gene (ldhA) with the L-(+)-Lactate Dehydrogenase Gene (ldhL) from Pediococcus acidilactici. Applied and Environmental Microbiology. 2003; 69(4):2237–44.
  • Zhou S, Causey TB, Hasona A, Shanmugam KT, Ingram LO: Production of Optically Pure D-Lactic Acid in Mineral Salts Medium by Metabolically Engineered Escherichia coli W3110. Applied and Environmental Microbiology. 2003; 69(1):399–407.
  • Zhu Y, Eiteman MA, DeWitt K, Altman E. Homolactate fermentation by metabolically engineered Escherichia coli strains. Appl Environ Microbiol. 2007; 73(2):456–64.
  • Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BO. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Molecular Systems Biology. 2007; 3:121.
  • Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, Palsson BO. A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Molecular Systems Biology. 2011; 7:535.
  • Mienda BS, Shamsir MS, Salleh FM. In silico metabolic engineering prediction of Escherichia coli genome model for production of D-lactic acid from glycerol using the OptFlux software platform. International Journal of Computational Bioinformatics and In Silico Modeling. 2014; 3(4):460–5.
  • Mienda BS, Shamsir MS. In silico Gene knockout metabolic interventions in Escherichia coli for Enhanced Ethanol production on Glycerol. Res J Pharm Biol Chem Sci. 2014; 5(4):964–74.
  • Mienda BS, Shamsir MS, Shehu I, Deba AA, Galadima IA. In Silico Metabolic Engineering Interventions of Escherichia Coli for Enhanced Ethanol Production, Based on Gene Knockout Simulation. IIOABJ. 2014; 5(2):16–23.
  • Rocha I, Maia P, Evangelista P, Vilaca P, Soares S, Pinto JP, Nielsen J, Patil KR, Ferreira EC, Rocha M: OptFlux: an open-source software platform for in silico metabolic engineering. BMC Systems Biology. 2010; 4:45.
  • Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, Palsson BO. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnology and Bioengineering. 2005; 91(5):643–8.
  • Rocha I, Maia P, Rocha M, Ferreira EC. OptGene - a framework for in silico metabolic engineering. Book of Abstracts of the 10th International Chemical and Biological Engineering Conference - CHEMPOR. 2008.
  • Vilaca P, Rocha I, Rocha M. A computational tool for the simulation and optimization of microbial strains accounting integrated metabolic/regulatory information. Bio Systems. 2011; 103(3):435–41.
  • Shlomi T, Berkman O, Ruppin E. Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(21):7695–700.
  • Monk J, Palsson BO. Genetics. Predicting microbial growth. Science 2014, 344(6191):1448-1449.
  • Mienda BS, Shamsir MS. Thermotolerant micro-organisms in Consolidated Bioprocessing for ethanol production: A review. Res Biotechnol. 2013; 4(4):1–6.
  • Lee SJ, Lee DY, Kim TY, Kim BH, Lee J, Lee SY. Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Appl Environ Microbiol 2005. 71(12):7880–7.
  • Lee DY, Yun H, Park S, Lee SY. MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics. 2003; 19(16):2144–6.
  • Feist AM, Zielinski DC, Orth JD, Schellenberger J, Herrgard MJ, Palsson BO. Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metabolic Engineering. 2010; 12(3):173–186.
  • McCloskey D, Palsson BO, Feist AM. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Molecular Systems Biology. 2013; 9:661.
  • Varma A, Boesch BW, Palsson BO. Stoichiometric Interpretation of Escherichia-coli Glucose Catabolism under Various Oxygenation Rates. Applied and Environmental Microbiology. 1993; 59(8):2465–73.
  • Edwards JS, Ibarra RU, Palsson BO. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotechnology. 2001; 19(2):125–30.
  • Fischer E, Zamboni N, Sauer U: High-throughput metabolic flux analysis based on gas chromatography-mass spectrometry derived 13C constraints. Analytical Biochemistry. 2004; 325(2):308–16.
  • Le Novere N, Bornstein B, Broicher A, Courtot M, Donizelli M, Dharuri H, Li L, Sauro H, Schilstra M, Shapiro B et al. BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical andcellular systems. Nucleic Acids Research. 2006; 34(Database issue):D689–91.
  • Zu Berstenhorst SM, Hohmann HP, Stahmann KP. Vitamins and Vitamin-like Compounds: Microbial Production. Encyclopedia of Microbiology. 3rd ed. Oxford: Academic Press; 2009. p. 549–61.
  • Alexeeva SBDK, Sawers G, Klaas J. Hellingwerf, Amjtd M. Effects of Limited Aeration and of the ArcAB System on intermediary metabolism in E. coli. Journal of Bacteriology. 2000; 182(17):4935–40.
  • Ndag S. Promoter 7 of the Escherichia coli pfl Operon Is a Major Determinant in the anaerobic Regulation of Expression by ArcA. J Bacteriol. 1995; 177(18):5338–41.


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