Applications of genome-scale metabolic network model in metabolic engineering

  • Byoungjin Kim
  • Won Jun Kim
  • Dong In Kim
  • Sang Yup LeeEmail author
Metabolic Engineering and Synthetic Biology


Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.


Genome-scale metabolic network Systems metabolic engineering Gene knock-out prediction Gene amplification prediction Metabolic pathway prediction Integrated genome-scale model 



This work was supported by the Intelligent Synthetic Biology Center through the Global Frontier Project (2011-0031963) of the Ministry of Science, ICT & Future Planning through the National Research Foundation of Korea.


  1. 1.
    Agren R, Liu LM, Shoaie S, Vongsangnak W, Nookaew I, Nielsen J (2013) The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput Biol 9(3):e1002980PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Alper H, Jin YS, Moxley JF, Stephanopoulos G (2005) Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab Eng 7(3):155–164PubMedCrossRefGoogle Scholar
  3. 3.
    Anna SB, Jason AP (2012) Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol 3:299Google Scholar
  4. 4.
    Bates JT, Chivian D, Arkin AP (2011) GLAMM: genome-linked application for metabolic maps. Nucleic Acids Res 39:W400–W405PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Becker SA, Palsson BØ (2008) Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 4(5):e1000082PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Bordbar A, Mo ML, Nakayasu ES, Schrimpe-Rutledge AC, Kim YM, Metz TO, Jones MB, Frank BC, Smith RD, Peterson SN et al (2012) Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation. Mol Syst Biol 8:558PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Brandes A, Lun DS, Ip K, Zucker J, Colijn C, Weiner B, Galagan JE (2012) Inferring carbon sources from gene expression profiles using metabolic flux models. PLoS One 7(5):e36947PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Brochado AR, Matos C, Moller BL, Hansen J, Mortensen UH, Patil KR (2010) Improved vanillin production in baker’s yeast through in silico design. Microb Cell Fact 9:84PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Buchel F, Rodriguez N, Swainston N, Wrzodek C, Czauderna T, Keller R, Mittag F, Schubert M, Glont M, Golebiewski M et al (2013) Path2Models: large-scale generation of computational models from biochemical pathway maps. BMC Syst Biol 7:116PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84(6):647–657PubMedCrossRefGoogle Scholar
  11. 11.
    Carbonell P, Planson AG, Fichera D, Faulon JL (2011) A retrosynthetic biology approach to metabolic pathway design for therapeutic production. BMC Syst Biol 5:112CrossRefGoogle Scholar
  12. 12.
    Carbonell P, Parutto P, Herisson J, Pandit SB, Faulon JL (2014) XTMS: pathway design in an extended metabolic space. Nucleic Acids Res. doi:101093/nar/gku362Google Scholar
  13. 13.
    Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci USA 107(41):17845–17850PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Cho A, Yun H, Park JH, Lee SY, Park S (2010) Prediction of novel synthetic pathways for the production of desired chemicals. BMC Syst Biol 4:35PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Choi HS, Lee SY, Kim TY, Woo HM (2010) In silico identification of gene amplification targets for improvement of lycopene production. Appl Environ Microbiol 76(10):3097–3105PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Chowdhury A, Zomorrodi AR, Maranas CD, Beard DA (2014) k-OptForce: integrating kinetics with flux balance analysis for strain design. PLoS Comput Biol 10(2):e1003487PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Chung BKS, Lee DY (2009) Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network. BMC Syst Biol 3:117PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, Cheng TY, Moody DB, Murray M, Galagan JE (2009) Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 5(8):e1000489PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Collins SB, Reznik E, Segre D (2012) Temporal expression-based analysis of metabolism. PLoS Comput Biol 8(11):e1002781PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Cotten C, Reed JL (2013) Constraint-based strain design using continuous modifications (CosMos) of flux bounds finds new strategies for metabolic engineering. Biotechnol J 8(5):595–604PubMedCrossRefGoogle Scholar
  21. 21.
    Dave L, Kieran S, Warwick BD, Ettore M, Catherine LW, Douglas BK, Pedro M, Neil S (2012) Improving metabolic flux predictions using absolute gene expression data. BMC Syst Biol 6:73CrossRefGoogle Scholar
  22. 22.
    Dikicioglu D, Pir P, Onsan ZI, Ulgen KO, Kirdar B, Oliver SG (2008) Integration of metabolic modeling and phenotypic data in evaluation and improvement of ethanol production using respiration-deficient mutants of Saccharomyces cerevisiae. Appl Environ Microb 74(18):5809–5816CrossRefGoogle Scholar
  23. 23.
    Edwards JS, Palsson BØ (2000) The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci USA 97(10):5528–5533PubMedCentralPubMedCrossRefGoogle Scholar
  24. 24.
    Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 3:121PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Flahaut NA, Wiersma A, van de Bunt B, Martens DE, Schaap PJ, Sijtsma L, Dos Santos VA, de Vos WM (2013) Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation. Appl Microbiol Biotechnol 97(19):8729–8739PubMedCrossRefGoogle Scholar
  26. 26.
    Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, Palsson BØ (2005) In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol Bioeng 91(5):643–648PubMedCrossRefGoogle Scholar
  27. 27.
    Forster J, Famili I, Fu P, Palsson BØ, Nielsen J (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13(2):244–253PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Hadicke O, Klamt S (2010) CASOP: a computational approach for strain optimization aiming at high productivity. J Biotechnol 147(2):88–101PubMedCrossRefGoogle Scholar
  29. 29.
    Hatzimanikatis V, Li CH, Ionita JA, Henry CS, Jankowski MD, Broadbelt LJ (2005) Exploring the diversity of complex metabolic networks. Bioinformatics 21(8):1603–1609PubMedCrossRefGoogle Scholar
  30. 30.
    Heavner BD, Smallbone K, Barker B, Mendes P, Walker LP (2012) Yeast 5—an expanded reconstruction of the Saccharomyces cerevisiae metabolic network. BMC Syst Biol 6:55PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Henry CS, Zinner JF, Cohoon MP, Stevens RL (2009) iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations. Genome Biol 10(6):R69PubMedCentralPubMedCrossRefGoogle Scholar
  32. 32.
    Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28(9):977–982PubMedCrossRefGoogle Scholar
  33. 33.
    Hnin WA, Susan AH, Larry PW (2013) Revising the representation of fatty acid, glycerolipid, and glycerophospholipid metabolism in the consensus model of yeast metabolism. Ind Biotechnol 9(4):215–228CrossRefGoogle Scholar
  34. 34.
    Hyduke DR, Lewis NE, Palsson BØ (2013) Analysis of omics data with genome-scale models of metabolism. Mol BioSyst 9(2):167–174PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Ines T, Neil S, Ronan MTF, Andreas H, Swagatika S, Maike KA, Hulda H, Monica LM, Ottar R, Miranda DS et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31(5):419–425CrossRefGoogle Scholar
  36. 36.
    Jan S, Richard Q, Ronan MTF, Ines T, Jeffrey DO, Adam MF, Daniel CZ, Aarash B, Nathan EL, Sorena R et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6(9):1290–1307CrossRefGoogle Scholar
  37. 37.
    Jensen PA, Lutz KA, Papin JA (2011) TIGER: toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks. BMC Syst Biol 5:147PubMedCentralPubMedCrossRefGoogle Scholar
  38. 38.
    Jensen PA, Papin JA (2011) Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 27(4):541–547PubMedCrossRefGoogle Scholar
  39. 39.
    Jerby L, Shlomi T, Ruppin E (2010) Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 6:401PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Monk JM, Charusanti P, Aziz RK, Lerman JA, Premyodhin N, Orth JD, Feist AM, Palsson BØ (2013) Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. Proc Natl Acad Sci USA 110(50):20338–20343PubMedCentralPubMedCrossRefGoogle Scholar
  41. 41.
    Jung YK, Kim TY, Park SJ, Lee SY (2010) Metabolic engineering of Escherichia coli for the production of polylactic acid and its copolymers. Biotechnol Bioeng 105(1):161–171PubMedCrossRefGoogle Scholar
  42. 42.
    Karp PD, Paley SM, Krummenacker M, Latendresse M, Dale JM, Lee TJ, Kaipa P, Gilham F, Spaulding A, Popescu L et al (2010) Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief Bioinform 11(1):40–79PubMedCentralPubMedCrossRefGoogle Scholar
  43. 43.
    Kim HU, Kim WJ, Lee SY (2013) Flux-coupled genes and their use in metabolic flux analysis. Biotechnol J 8(9):1035–1042PubMedCrossRefGoogle Scholar
  44. 44.
    Kim J, Reed JL (2010) OptORF: optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst Biol 4:53PubMedCentralPubMedCrossRefGoogle Scholar
  45. 45.
    Kim J, Reed JL (2012) RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations. Genome Biol 13(9):R78PubMedCentralPubMedCrossRefGoogle Scholar
  46. 46.
    Kim TY, Kim HU, Park JM, Song H, Kim JS, Lee SY (2007) Genome-scale analysis of Mannheimia succiniciproducens metabolism. Biotechnol Bioeng 97(4):657–671PubMedCrossRefGoogle Scholar
  47. 47.
    Lakshmanan M, Chung BKS, Liu CC, Kim SW, Lee DY (2013) Cofactor modification analysis: a computational framework to identify cofactor specificity engineering targets for strain improvement. J Bioinf Comput Biol 11(6):1343006CrossRefGoogle Scholar
  48. 48.
    Lee J, Yun H, Feist AM, Palsson BØ, Lee SY (2008) Genome-scale reconstruction and in silico analysis of the Clostridium acetobutylicum ATCC 824 metabolic network. Appl Microbiol Biotechnol 80(5):849–862PubMedCrossRefGoogle Scholar
  49. 49.
    Lee KY, Park JM, Kim TY, Yun H, Lee SY (2010) The genome-scale metabolic network analysis of Zymomonas mobilis ZM4 explains physiological features and suggests ethanol and succinic acid production strategies. Microb Cell Fact 9:94PubMedCentralPubMedCrossRefGoogle Scholar
  50. 50.
    Lerman JA, Hyduke DR, Latif H, Portnoy VA, Lewis NE, Orth JD, Schrimpe-Rutledge AC, Smith RD, Adkins JN, Zengler K et al (2012) In silico method for modelling metabolism and gene product expression at genome scale. Nat Commun 3:929PubMedCrossRefGoogle Scholar
  51. 51.
    Lerman JA, Chang RL, Hyduke DR (2013) Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 9:693PubMedCentralPubMedGoogle Scholar
  52. 52.
    Lewis NE, Nagarajan H, Palsson BØ (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10(4):291–305PubMedCentralPubMedGoogle Scholar
  53. 53.
    Lun DS, Rockwell G, Guido NJ, Baym M, Kelner JA, Berger B, Galagan JE, Church GM (2009) Large-scale identification of genetic design strategies using local search. Mol Syst Biol 5:296PubMedCentralPubMedCrossRefGoogle Scholar
  54. 54.
    Machado D, Herrgard M (2014) Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 10(4):e1003580PubMedCentralPubMedCrossRefGoogle Scholar
  55. 55.
    Melzer G, Esfandabadi ME, Franco-Lara E, Wittmann C (2009) Flux design: in silico design of cell factories based on correlation of pathway fluxes to desired properties. BMC Syst Biol 3:120PubMedCentralPubMedCrossRefGoogle Scholar
  56. 56.
    Campodonico MA, Andrews BA, Asenjo JA, Palsson BØ, Feist AM (2014) Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm, GEM-Path. Metab Eng 25:140–158PubMedCrossRefGoogle Scholar
  57. 57.
    Milne CB, Eddy JA, Raju R, Ardekani S, Kim P-J, Senger RS, Jin Y-S, Blaschek HP, Price ND (2011) Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052. BMC Syst Biol 5:130PubMedCentralPubMedCrossRefGoogle Scholar
  58. 58.
    Monk J, Nogales J, Palsson BØ (2014) Optimizing genome-scale network reconstructions. Nat Biotechnol 32(5):447–452PubMedCrossRefGoogle Scholar
  59. 59.
    Moriya Y, Shigemizu D, Hattori M, Tokimatsu T, Kotera M, Goto S, Kanehisa M (2010) PathPred: an enzyme-catalyzed metabolic pathway prediction server. Nucleic Acids Res 38:W138–W143PubMedCentralPubMedCrossRefGoogle Scholar
  60. 60.
    Navid A, Almaas E (2012) Genome-level transcription data of Yersinia pestis analyzed with a new metabolic constraint-based approach. BMC Syst Biol 6:150PubMedCentralPubMedCrossRefGoogle Scholar
  61. 61.
    Nocon J, Steiger MG, Pfeffer M, Sohn SB, Kim TY, Maurer M, Russmayer H, Pflugl S, Ask M, Haberhauer-Troyer C et al (2014) Model based engineering of Pichia pastoris central metabolism enhances recombinant protein production. Metab Eng 24:129–138PubMedCentralPubMedCrossRefGoogle Scholar
  62. 62.
    Orth JD, Conrad TM, Na J, Lerman JA (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol Syst Biol 7:535PubMedCentralPubMedCrossRefGoogle Scholar
  63. 63.
    Österlund T, Nookaew I, Bordel S (2013) Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling. BMC Syst Biol 7:36PubMedCentralPubMedCrossRefGoogle Scholar
  64. 64.
    Park JH, Lee KH, Kim TY, Lee SY (2007) Metabolic engineering of Escherichia coli for the production of l-valine based on transcriptome analysis and in silico gene knockout simulation. Proc Natl Acad Sci USA 104(19):7797–7802PubMedCentralPubMedCrossRefGoogle Scholar
  65. 65.
    Park JM, Park HM, Kim WJ, Kim HU, Kim TY, Lee SY (2012) Flux variability scanning based on enforced objective flux for identifying gene amplification targets. BMC Syst Biol 6:106PubMedCentralPubMedCrossRefGoogle Scholar
  66. 66.
    Patil KR, Rocha I, Forster J, Nielsen J (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinform 6:308CrossRefGoogle Scholar
  67. 67.
    Pharkya P, Burgard AP, Maranas CD (2004) OptStrain: a computational framework for redesign of microbial production systems. Genome Res 14(11):2367–2376PubMedCentralPubMedCrossRefGoogle Scholar
  68. 68.
    Pharkya P, Maranas CD (2006) An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab Eng 8(1):1–13PubMedCrossRefGoogle Scholar
  69. 69.
    Puchałka J, Oberhardt MA, Godinho M, Bielecka A, Regenhardt D, Timmis KN, Papin JA, Martins dos Santos VA (2008) Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology. PLoS Comput Biol 4:10CrossRefGoogle Scholar
  70. 70.
    Raman K, Chandra N (2009) Flux balance analysis of biological systems: applications and challenges. Brief Bioinform 10(4):435–449PubMedCrossRefGoogle Scholar
  71. 71.
    Ranganathan S, Maranas CD (2010) Microbial 1-butanol production: identification of non-native production routes and in silico engineering interventions. Biotechnol J 5(7):716–725PubMedCrossRefGoogle Scholar
  72. 72.
    Ranganathan S, Suthers PF, Maranas CD (2010) OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput Biol 6(4):e1000744PubMedCentralPubMedCrossRefGoogle Scholar
  73. 73.
    Rocha I, Maia P, Evangelista P, Vilaca P, Soares S, Pinto JP, Nielsen J, Patil KR, Ferreira EC, Rocha M (2010) OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst Biol 4:45PubMedCentralPubMedCrossRefGoogle Scholar
  74. 74.
    Rodrigo G, Carrera J, Prather KJ, Jaramillo A (2008) DESHARKY: automatic design of metabolic pathways for optimal cell growth. Bioinformatics 24(21):2554–2556PubMedCrossRefGoogle Scholar
  75. 75.
    Rossell S, Huynen MA, Notebaart RA (2013) Inferring metabolic states in uncharacterized environments using gene-expression measurements. PLoS Comput Biol 9(3):e1002988PubMedCentralPubMedCrossRefGoogle Scholar
  76. 76.
    Schmidt BJ, Ebrahim A, Metz TO, Adkins JN, Palsson BØ, Hyduke DR (2013) GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data. Bioinformatics 29(22):2900–2908PubMedCentralPubMedCrossRefGoogle Scholar
  77. 77.
    Schuetz R, Zamboni N, Zampieri M, Heinemann M, Sauer U (2012) Multidimensional optimality of microbial metabolism. Science 336(6081):601–604PubMedCrossRefGoogle Scholar
  78. 78.
    Segre D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci USA 99(23):15112–15117PubMedCentralPubMedCrossRefGoogle Scholar
  79. 79.
    Selvarasu S, Ho YS, Chong WPK, Wong NSC, Yusufi FNK, Lee YY, Yap MGS, Lee D-Y (2012) Combined in silico modeling and metabolomics analysis to characterize fed-batch CHO cell culture. Biotechnol Bioeng 109(6):1415–1429PubMedCrossRefGoogle Scholar
  80. 80.
    Shinfuku Y, Sorpitiporn N, Sono M, Furusawa C, Hirasawa T, Shimizu H (2009) Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum. Microb Cell Fact 8:43PubMedCentralPubMedCrossRefGoogle Scholar
  81. 81.
    Shlomi T, Berkman O, Ruppin E (2005) Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc Natl Acad Sci USA 102(21):7695–7700PubMedCentralPubMedCrossRefGoogle Scholar
  82. 82.
    Shlomi T, Cabili MN, Herrgard MJ, Palsson BØ, Ruppin E (2008) Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26(9):1003–1010PubMedCrossRefGoogle Scholar
  83. 83.
    Sohn SB, Graf AB, Kim TY, Gasser B, Maurer M, Ferrer P, Mattanovich D, Lee SY (2010) Genome-scale metabolic model of methylotrophic yeast Pichia pastoris and its use for in silico analysis of heterologous protein production. Biotechnol J 5(7):705–715PubMedCrossRefGoogle Scholar
  84. 84.
    Song CW, Kim DI, Choi S, Jang JW, Lee SY (2013) Metabolic engineering of Escherichia coli for the production of fumaric acid. Biotechnol Bioeng 110(7):2025–2034PubMedCrossRefGoogle Scholar
  85. 85.
    Swainston N, Smallbone K, Mendes P, Kell DB, Paton NW (2011) The SuBliMinaL toolbox: automating steps in the reconstruction of metabolic networks. J Integr Bioinform 8:186PubMedGoogle Scholar
  86. 86.
    Tepper N, Shlomi T (2010) Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics 26(4):536–543PubMedCrossRefGoogle Scholar
  87. 87.
    Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5(1):93–121PubMedCentralPubMedCrossRefGoogle Scholar
  88. 88.
    Thiele I, Hyduke DR, Steeb B, Fankam G, Allen DK, Bazzani S, Charusanti P, Chen F-CC, Fleming RM, Hsiung CA et al (2011) A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2. BMC Syst Biol 5:8PubMedCentralPubMedCrossRefGoogle Scholar
  89. 89.
    Topfer N, Jozefczuk S, Nikoloski Z (2012) Integration of time-resolved transcriptomics data with flux-based methods reveals stress-induced metabolic adaptation in Escherichia coli. BMC Syst Biol 6:148PubMedCentralPubMedCrossRefGoogle Scholar
  90. 90.
    van Berlo RJP, de Ridder D, Daran JM, Daran-Lapujade PAS, Teusink B, Reinders MJT (2011) Predicting metabolic fluxes using gene expression differences as constraints. IEEE ACM Trans Comput Biol 8(1):206–216CrossRefGoogle Scholar
  91. 91.
    Vu TT, Hill EA, Kucek LA, Konopka AE, Beliaev AS, Reed JL (2013) Computational evaluation of Synechococcus sp. PCC 7002 metabolism for chemical production. Biotechnol J 8(5):619–630PubMedCrossRefGoogle Scholar
  92. 92.
    Wang YL, Eddy JA, Price ND (2012) Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst Biol 6:153PubMedCentralPubMedCrossRefGoogle Scholar
  93. 93.
    Yang L, Cluett WR, Mahadevan R (2011) EMILiO: a fast algorithm for genome-scale strain design. Metab Eng 13(3):272–281PubMedCrossRefGoogle Scholar
  94. 94.
    Yim H, Haselbeck R, Niu W, Pujol-Baxley C, Burgard A, Boldt J, Khandurina J, Trawick JD, Osterhout RE, Stephen R et al (2011) Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nat Chem Biol 7(7):445–452PubMedCrossRefGoogle Scholar
  95. 95.
    Zachary AK, Adam MF (2013) Optimizing cofactor specificity of oxidoreductase enzymes for the generation of microbial production strains—OptSwap. Ind Biotechnol 9(4):236–246CrossRefGoogle Scholar

Copyright information

© Society for Industrial Microbiology and Biotechnology 2014

Authors and Affiliations

  • Byoungjin Kim
    • 1
  • Won Jun Kim
    • 1
  • Dong In Kim
    • 1
  • Sang Yup Lee
    • 1
    Email author
  1. 1.Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic BiotechnologyInstitute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea

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