Neisseria meningitidis pp 107-126

Part of the Methods in Molecular Biology book series (MIMB, volume 799)

Genome-Scale Metabolic Models: Reconstruction and Analysis



Metabolism can be defined as the complete set of chemical reactions that occur in living organisms in order to maintain life. Enzymes are the main players in this process as they are responsible for catalyzing the chemical reactions. The enzyme–reaction relationships can be used for the reconstruction of a network of reactions, which leads to a metabolic model of metabolism. A genome-scale metabolic network of chemical reactions that take place inside a living organism is primarily reconstructed from the information that is present in its genome and the literature and involves steps such as functional annotation of the genome, identification of the associated reactions and determination of their stoichiometry, assignment of localization, determination of the biomass composition, estimation of energy requirements, and definition of model constraints. This information can be integrated into a stoichiometric model of metabolism that can be used for detailed analysis of the metabolic potential of the organism using constraint-based modeling approaches and hence is valuable in understanding its metabolic capabilities.

Key words

Genome-scale metabolic network reconstruction Metabolic networks Metabolic flux analysis Flux balance analysis Constraint-based modeling 


  1. 1.
    Baart GJ, Zomer B, de Haan A et al (2007) Modeling Neisseria meningitidismetabolism: from genome to metabolic fluxes. Genome Biol 8: R136.PubMedCrossRefGoogle Scholar
  2. 2.
    Price ND, Papin JA, Schilling CH et al (2003) Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol 21: 162–169.PubMedCrossRefGoogle Scholar
  3. 3.
    Kim HU, Kim TY, Lee, SY (2007) Metabolic flux analysis and metabolic engineering of microorganisms. Mol BioSyst 4: 113–120.PubMedCrossRefGoogle Scholar
  4. 4.
    Baart GJE, Willemsen M, Khatami E et al (2008) Modeling Neisseria meningitidisB metabolism at different specific growth rates. Biotechnol Bioeng 101: 1022–1035.PubMedCrossRefGoogle Scholar
  5. 5.
    Hua Q, Joyce AR, Fong SS et al (2006) Metabolic analysis of adaptive evolution for in silico-designed lactate-producing strains. Biotechnol Bioeng 95: 992–1002.PubMedCrossRefGoogle Scholar
  6. 6.
    Fong SS, Burgard AP, Herring CD et al (2005) In silico design and adaptive evolution of Escherichia colifor production of lactic acid. Biotechnol Bioeng 91: 643–648.PubMedCrossRefGoogle Scholar
  7. 7.
    Smid EJ, Molenaar D, Hugenholtz J et al (2005) Functional ingredient production: application of global metabolic models. Curr Opin Biotechnol 16: 190–197.PubMedCrossRefGoogle Scholar
  8. 8.
    Baart GJ, Langenhof M, van de Waterbeemd B et al (2010) Expression of phosphofructokinase in Neisseria meningitidis. Microbiology 156: 530–542.PubMedCrossRefGoogle Scholar
  9. 9.
    Teusink B, van Enckevort FH, Francke C et al (2005) In silico reconstruction of the metabolic pathways of Lactobacillus plantarum: comparing predictions of nutrient requirements with those from growth experiments. Appl Environ Microbiol 71: 7253–7262.PubMedCrossRefGoogle Scholar
  10. 10.
    Xie L, Wang DIC (1994) Stoichiometric analysis of animal cell growth and its application in medium design. Biotechnol Bioeng 43: 1164–1174.PubMedCrossRefGoogle Scholar
  11. 11.
    Provost A, Bastin G (2004) Dynamic metabolic modelling under the balanced growth condition. J Proc Control 14: 717–728.CrossRefGoogle Scholar
  12. 12.
    Covert MW, Schilling CH, Famili I et al (2001) Metabolic modeling of microbial strains in silico. Trends Biochem Sci 26: 179–186.PubMedCrossRefGoogle Scholar
  13. 13.
    Francke C, Siezen RJ, Teusink B (2005) Reconstructing the metabolic network of a bacterium from its genome. Trends Microbiol 13: 550–558.PubMedCrossRefGoogle Scholar
  14. 14.
    Ostlund G, Schmitt T, Forslund K et al (2010) InParanoid 7: new algorithms and tools for eukaryotic orthology analysis. Nucleic Acids Res 38: D196–203.PubMedCrossRefGoogle Scholar
  15. 15.
    Notebaart RA, van Enckevort FH, Francke C et al (2006) Accelerating the reconstruction of genome-scale metabolic networks. BMC Bioinformatics 7: 296.PubMedCrossRefGoogle Scholar
  16. 16.
    Herrgard MJ, Fong SS, Palsson BO (2006) Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Comput Biol 2: e72.PubMedCrossRefGoogle Scholar
  17. 17.
    Karp PD, Paley S, Romero P (2002) The Pathway Tools software. Bioinformatics 18 Suppl 1: S225–232.PubMedCrossRefGoogle Scholar
  18. 18.
    Moriya Y, Itoh M, Okuda S et al (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 35: W182–185.PubMedCrossRefGoogle Scholar
  19. 19.
    Pinney JW, Shirley MW, McConkey GA et al (2005) metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparumand Eimeria tenella. Nucleic Acids Res 33: 1399–1409.PubMedCrossRefGoogle Scholar
  20. 20.
    Sun J, Zeng AP (2004) IdentiCS – identification of coding sequence and in silico reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence. BMC Bioinformatics 5: 112.PubMedCrossRefGoogle Scholar
  21. 21.
    Zhang KX, Ouellette BF (2010) Pandora, a pathway and network discovery approach based on common biological evidence. Bioinformatics 26: 529–535.PubMedCrossRefGoogle Scholar
  22. 22.
    Caspi R, Altman T, Dale JM et al (2010) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 38: D473–479.PubMedCrossRefGoogle Scholar
  23. 23.
    Keseler IM, Collado-Vides J, Gama-Castro S et al (2005) EcoCyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res 33: D334–337.PubMedCrossRefGoogle Scholar
  24. 24.
    Mueller LA, Zhang P, Rhee SY (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol 132: 453–460.PubMedCrossRefGoogle Scholar
  25. 25.
    Romero P, Karp P (2003) PseudoCyc, a pathway-genome database for Pseudomonas aeruginosa. J Mol Microbiol Biotechnol 5: 230–239.PubMedCrossRefGoogle Scholar
  26. 26.
    Romero P, Wagg J, Green ML et al (2005) Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 6: R2.PubMedCrossRefGoogle Scholar
  27. 27.
    Zhu G, Golding GB, Dean AM (2005) The selective cause of an ancient adaptation. Science 307: 1279–1282.PubMedCrossRefGoogle Scholar
  28. 28.
    Zamboni N, Kummel A, Heinemann M (2008) anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data. BMC Bioinformatics 9: 199.PubMedCrossRefGoogle Scholar
  29. 29.
    Neidhardt FC, Umbarger HE (1996) Chemical composition of Escherichia coli, In Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology(Neidhardt FC, Curtiss R, Ingraham JL, Brooks Low K, Magasanik B, Reznikoff WS, Riley M, Schaechter M, Umbarger HE, Eds.) 2 ed., pp 13–16, American Society for Microbiology, Washington.Google Scholar
  30. 30.
    Taymaz-Nikerel H, Borujeni AE, Verheijen PJ et al (2010) Genome-derived minimal metabolic models for Escherichia coliMG1655 with estimated in vivo respiratory ATP stoichiometry. Biotechnol Bioeng 107: 369–381.PubMedCrossRefGoogle Scholar
  31. 31.
    Novak L, Loubiere P (2000) The metabolic network of Lactococcus lactis: distribution of (14)C-labeled substrates between catabolic and anabolic pathways. J Bacteriol 182: 1136–1143.PubMedCrossRefGoogle Scholar
  32. 32.
    Albers E, Larsson C, Andlid T et al (2007) Effect of nutrient starvation on the cellular composition and metabolic capacity of Saccharomyces cerevisiae. Appl Environ Microbiol 73: 4839–4848.PubMedCrossRefGoogle Scholar
  33. 33.
    Cortassa S, Aon JC, Aon MA (1995) Fluxes of carbon, phosphorylation, and redox intermediates during growth of Saccharomyces cerevisiaeon different carbon sources. Biotechnol Bioeng 47: 193–208.PubMedCrossRefGoogle Scholar
  34. 34.
    Herwig C, Von Stockar U (2003) Quantitative comparison of transient growth of Saccharomyces cerevisiae, Saccharomyces kluyveri, and Kluyveromyces lactis. Biotechnol Bioeng 81: 837–847.PubMedCrossRefGoogle Scholar
  35. 35.
    Hjersted JL, Henson MA (2009) Steady-state and dynamic flux balance analysis of ethanol production by Saccharomyces cerevisiae. IET Syst Biol 3: 167–179.PubMedCrossRefGoogle Scholar
  36. 36.
    Nissen TL, Schulze U, Nielsen J et al (1997) Flux distributions in anaerobic, glucose-limited continuous cultures of Saccharomyces cerevisiae. Microbiology 143 (Pt 1): 203–218.PubMedCrossRefGoogle Scholar
  37. 37.
    Verduyn C, Postma E, Scheffers WA et al (1990) Energetics of Saccharomyces cerevisiaein anaerobic glucose-limited chemostat cultures. J Gen Microbiol 136: 405–412.PubMedGoogle Scholar
  38. 38.
    Wisselink HW, Cipollina C, Oud B et al (2010) Metabolome, transcriptome and metabolic flux analysis of arabinose fermentation by engineered Saccharomyces cerevisiae. Metab Eng 12(6):537–5178.PubMedCrossRefGoogle Scholar
  39. 39.
    Nasution U, van Gulik WM, Ras C et al (2008) A metabolome study of the steady-state relation between central metabolism, amino acid biosynthesis and penicillin production in Penicillium chrysogenum. Metab Eng 10: 10–23.PubMedCrossRefGoogle Scholar
  40. 40.
    Carnicer M, Baumann K, Toplitz I et al (2009) Macromolecular and elemental composition analysis and extracellular metabolite balances of Pichia pastorisgrowing at different oxygen levels. Microb Cell Fact 8: 65.PubMedCrossRefGoogle Scholar
  41. 41.
    Osterman A, Overbeek R (2003) Missing genes in metabolic pathways: a comparative genomics approach. Curr Opin Chem Biol 7: 238–251.PubMedCrossRefGoogle Scholar
  42. 42.
    Kharchenko P, Vitkup D, Church GM (2004) Filling gaps in a metabolic network using expression information. Bioinformatics 20 Suppl 1: i178–185.PubMedCrossRefGoogle Scholar
  43. 43.
    Forster J, Famili I, Fu P et al (2003) Genome-scale reconstruction of the Saccharomyces cerevisiaemetabolic network. Genome Res 13: 244–253.PubMedCrossRefGoogle Scholar
  44. 44.
    van Gulik WM (2010) Metabolic models for growth and product formation, In The Metabolic Pathway Engineering Handbook(Smolke CD, Ed.), CRC press, Boca Raton.Google Scholar
  45. 45.
    van Gulik WM, Antoniewicz MR, deLaat WT, et al (2001) Energetics of growth and penicillin production in a high-producing strain of Penicillium chrysogenum. Biotechnol Bioeng 72: 185–193.CrossRefGoogle Scholar
  46. 46.
    Vanrolleghem PA, Heijnen JJ (1998) A structured approach for selection among candidate metabolic network models and estimation of unknown stoichiometric coefficients. Biotechnol Bioeng 58: 133–138.PubMedCrossRefGoogle Scholar
  47. 47.
    Covert MW, Knight EM, Reed JL et al (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature 429: 92–96.PubMedCrossRefGoogle Scholar
  48. 48.
    Bruggeman FJ, Snoep JL, Westerhoff HV (2008) Control, responses and modularity of cellular regulatory networks: a control analysis perspective. IET Syst Biol 2: 397–410.PubMedCrossRefGoogle Scholar
  49. 49.
    Bruggeman FJ, Westerhoff HV (2006) Approaches to biosimulation of cellular processes. J Biol Phys 32: 273–288.PubMedCrossRefGoogle Scholar
  50. 50.
    De Mey M, Taymaz-Nikerel H, Baart G et al (2010) Catching prompt metabolite dynamics in Escherichia coliwith the BioScope at oxygen rich conditions. Metab Eng 12: 477–487.PubMedCrossRefGoogle Scholar
  51. 51.
    Tomita M (2001) Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol 19: 205–210.PubMedCrossRefGoogle Scholar
  52. 52.
    Young JD, Henne KL, Morgan JA et al (2008) Integrating cybernetic modeling with pathway analysis provides a dynamic, systems-level description of metabolic control. Biotechnol Bioeng 100: 542–559.PubMedCrossRefGoogle Scholar
  53. 53.
    Edwards JS, Covert M, Palsson B (2002) Metabolic modelling of microbes: the flux-balance approach. Environ Microbiol 4: 133–140.PubMedCrossRefGoogle Scholar
  54. 54.
    Henriksen CM, Christensen LH, Nielsen J et al (1996) Growth energetics and metabolic fluxes in continuous cultures of Penicillium chrysogenum. J Biotechnol 45: 149–164.PubMedCrossRefGoogle Scholar
  55. 55.
    Kayser A, Weber J, Hecht V et al (2005) Metabolic flux analysis of Escherichia coliin glucose-limited continuous culture. I. Growth-rate-dependent metabolic efficiency at steady state. Microbiology 151: 693–706.PubMedCrossRefGoogle Scholar
  56. 56.
    Oliveira AP, Nielsen J, Forster J (2005) Modeling Lactococcus lactisusing a genome-scale flux model. BMC Microbiol 5: 39.PubMedCrossRefGoogle Scholar
  57. 57.
    Teusink B, Smid EJ (2006) Modelling strategies for the industrial exploitation of lactic acid bacteria. Nat Rev Microbiol 4: 46–56.PubMedCrossRefGoogle Scholar
  58. 58.
    Schuster S, Dandekar T, Fell DA (1999) Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol 17: 53–60.PubMedCrossRefGoogle Scholar
  59. 59.
    Schilling CH, Schuster S, Palsson BO et al (1999) Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnol Prog 15: 296–303.PubMedCrossRefGoogle Scholar
  60. 60.
    Christensen B, Gombert AK, Nielsen J (2002) Analysis of flux estimates based on 13C-labelling experiments. Eur J Biochem 269: 2795–2800.PubMedCrossRefGoogle Scholar
  61. 61.
    Edwards JS, Ibarra RU, Palsson BO (2001) In silico predictions of Escherichia colimetabolic capabilities are consistent with experimental data. Nat Biotechnol 19: 125–130.PubMedCrossRefGoogle Scholar
  62. 62.
    Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3 (article 119): 1–15.Google Scholar
  63. 63.
    Edwards JS, Palsson BO (2000) Metabolic flux balance analysis and the in silico analysis of Escherichia coliK-12 gene deletions. BMC Bioinformatics 1: 1.PubMedCrossRefGoogle Scholar
  64. 64.
    Edwards JS, Ramakrishna R, Palsson BO (2002) Characterizing the metabolic phenotype: a phenotype phase plane analysis. Biotechnol Bioeng 77: 27–36.PubMedCrossRefGoogle Scholar
  65. 65.
    Famili I, Forster J, Nielsen J et al (2003) Saccharomyces cerevisiaephenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc Natl Acad Sci U S A 100: 13134–13139.PubMedCrossRefGoogle Scholar
  66. 66.
    Forster J, Famili I, Palsson BO et al (2003) Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. Omics 7: 193–202.PubMedCrossRefGoogle Scholar
  67. 67.
    van der Heijden RTJM, Romein B, Heijnen JJ et al (1994) Linear constraint relations in biochemical reaction systems: I. Classification of the calculability and the balanceability of conversion rates. Biotech Bioeng 43: 3–10.CrossRefGoogle Scholar
  68. 68.
    van der Heijden RTJM, Romein B, Heijnen JJ et al (1994) Linear constraint relations in biochemical reaction systems: II. Diagnosis and estimation of gross errors. Biotech Bioeng 43: 11–20.CrossRefGoogle Scholar
  69. 69.
    Rocha I, Forster J, Nielsen J (2008) Design and application of genome-scale reconstructed metabolic networks, In Microbial Gene Essentiality: Protocals and Bioinformatics(Osterman AL, Gerdes SY, Eds.), pp 409–431, Humana Press, Totowa.Google Scholar
  70. 70.
    Bonarius HPJ, Schmid G, Tramper J (1997) Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. Trends Biotechnol 15: 308–314.CrossRefGoogle Scholar
  71. 71.
    Klamt S, Stelling J, Ginkel M et al (2003) FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps. Bioinformatics 19: 261–269.PubMedCrossRefGoogle Scholar
  72. 72.
    Klamt S, Saez-Rodriguez J, Gilles ED (2007) Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol 1: 2.PubMedCrossRefGoogle Scholar
  73. 73.
    Lee DY, Yun H, Park S et al (2003) MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics 19: 2144–2146.PubMedCrossRefGoogle Scholar
  74. 74.
    Lee SY, Lee DY, Hong SH, et al (2003) MetaFluxNet, a program package for metabolic pathway construction and analysis, and its use in large-scale metabolic flux analysis of Escherichia coli. Genome Inform 14: 23–33.PubMedGoogle Scholar
  75. 75.
    Becker SA, Feist AM, Mo ML et al (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2: 727–738.PubMedCrossRefGoogle Scholar
  76. 76.
    Kitano H, Funahashi A, Matsuoka Y et al (2005) Using process diagrams for the graphical representation of biological networks. Nat Biotechnol 23: 961–966.PubMedCrossRefGoogle Scholar
  77. 77.
    Funahashi A, Tanimura N, Morohashi M et al (2003) CellDesigner: a process diagram editor for gene-regulatory and biochemical networks. BIOSILICO 1: 159–162.CrossRefGoogle Scholar
  78. 78.
    Schilling C, Thakar R, Travnik E et al (2008) SimPheny™: A Computational Infrastructure for Systems Biology, In Genomics: GTL Contractor—Grantee Workshop III, pp 67–68, U.S. Department of Energy, Washington.Google Scholar
  79. 79.
    Zamboni N, Fischer E, Sauer U (2005) FiatFlux – a software for metabolic flux analysis from 13C-glucose experiments. BMC Bioinformatics 6: 209.PubMedCrossRefGoogle Scholar
  80. 80.
    Wiechert W, Mollney M, Petersen S et al (2001) A universal framework for 13C metabolic flux analysis. Metab Eng 3: 265–283.PubMedCrossRefGoogle Scholar
  81. 81.
    Quek LE, Wittmann C, Nielsen LK et al (2009) OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis. Microb Cell Fact 8: 25.PubMedCrossRefGoogle Scholar
  82. 82.
    Pfeiffer T, Sanchez-Valdenebro I, Nuno JC et al (1999) METATOOL: for studying metabolic networks. Bioinformatics 15: 251–257.PubMedCrossRefGoogle Scholar
  83. 83.
    Schwarz R, Liang C, Kaleta C et al (2007) Integrated network reconstruction, visualization and analysis using YANAsquare. BMC Bioinformatics 8: 313.PubMedCrossRefGoogle Scholar
  84. 84.
    Schwarz R, Musch P, von Kamp A et al (2005) YANA - a software tool for analyzing flux modes, gene-expression and enzyme activities. BMC Bioinformatics 6: 135.PubMedCrossRefGoogle Scholar
  85. 85.
    Bell SL, Palsson BO (2005) Expa: a program for calculating extreme pathways in biochemical reaction networks. Bioinformatics 21: 1739–1740.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.VIB Department of Plant Systems Biology/Department of Biology, Protistology and Aquatic EcologyGhent UniversityGhentBelgium
  2. 2.Bioprocess Engineering GroupWageningen UniversityWageningenThe Netherlands

Personalised recommendations