Abstract
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.
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References
Baart GJ, Zomer B, de Haan A et al (2007) Modeling Neisseria meningitidismetabolism: from genome to metabolic fluxes. Genome Biol 8: R136.
Price ND, Papin JA, Schilling CH et al (2003) Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol 21: 162–169.
Kim HU, Kim TY, Lee, SY (2007) Metabolic flux analysis and metabolic engineering of microorganisms. Mol BioSyst 4: 113–120.
Baart GJE, Willemsen M, Khatami E et al (2008) Modeling Neisseria meningitidisB metabolism at different specific growth rates. Biotechnol Bioeng 101: 1022–1035.
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.
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.
Smid EJ, Molenaar D, Hugenholtz J et al (2005) Functional ingredient production: application of global metabolic models. Curr Opin Biotechnol 16: 190–197.
Baart GJ, Langenhof M, van de Waterbeemd B et al (2010) Expression of phosphofructokinase in Neisseria meningitidis. Microbiology 156: 530–542.
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.
Xie L, Wang DIC (1994) Stoichiometric analysis of animal cell growth and its application in medium design. Biotechnol Bioeng 43: 1164–1174.
Provost A, Bastin G (2004) Dynamic metabolic modelling under the balanced growth condition. J Proc Control 14: 717–728.
Covert MW, Schilling CH, Famili I et al (2001) Metabolic modeling of microbial strains in silico. Trends Biochem Sci 26: 179–186.
Francke C, Siezen RJ, Teusink B (2005) Reconstructing the metabolic network of a bacterium from its genome. Trends Microbiol 13: 550–558.
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.
Notebaart RA, van Enckevort FH, Francke C et al (2006) Accelerating the reconstruction of genome-scale metabolic networks. BMC Bioinformatics 7: 296.
Herrgard MJ, Fong SS, Palsson BO (2006) Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Comput Biol 2: e72.
Karp PD, Paley S, Romero P (2002) The Pathway Tools software. Bioinformatics 18 Suppl 1: S225–232.
Moriya Y, Itoh M, Okuda S et al (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 35: W182–185.
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.
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.
Zhang KX, Ouellette BF (2010) Pandora, a pathway and network discovery approach based on common biological evidence. Bioinformatics 26: 529–535.
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.
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.
Mueller LA, Zhang P, Rhee SY (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol 132: 453–460.
Romero P, Karp P (2003) PseudoCyc, a pathway-genome database for Pseudomonas aeruginosa. J Mol Microbiol Biotechnol 5: 230–239.
Romero P, Wagg J, Green ML et al (2005) Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 6: R2.
Zhu G, Golding GB, Dean AM (2005) The selective cause of an ancient adaptation. Science 307: 1279–1282.
Zamboni N, Kummel A, Heinemann M (2008) anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data. BMC Bioinformatics 9: 199.
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.
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.
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.
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.
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.
Herwig C, Von Stockar U (2003) Quantitative comparison of transient growth of Saccharomyces cerevisiae, Saccharomyces kluyveri, and Kluyveromyces lactis. Biotechnol Bioeng 81: 837–847.
Hjersted JL, Henson MA (2009) Steady-state and dynamic flux balance analysis of ethanol production by Saccharomyces cerevisiae. IET Syst Biol 3: 167–179.
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.
Verduyn C, Postma E, Scheffers WA et al (1990) Energetics of Saccharomyces cerevisiaein anaerobic glucose-limited chemostat cultures. J Gen Microbiol 136: 405–412.
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.
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.
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.
Osterman A, Overbeek R (2003) Missing genes in metabolic pathways: a comparative genomics approach. Curr Opin Chem Biol 7: 238–251.
Kharchenko P, Vitkup D, Church GM (2004) Filling gaps in a metabolic network using expression information. Bioinformatics 20 Suppl 1: i178–185.
Forster J, Famili I, Fu P et al (2003) Genome-scale reconstruction of the Saccharomyces cerevisiaemetabolic network. Genome Res 13: 244–253.
van Gulik WM (2010) Metabolic models for growth and product formation, In The Metabolic Pathway Engineering Handbook(Smolke CD, Ed.), CRC press, Boca Raton.
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.
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.
Covert MW, Knight EM, Reed JL et al (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature 429: 92–96.
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.
Bruggeman FJ, Westerhoff HV (2006) Approaches to biosimulation of cellular processes. J Biol Phys 32: 273–288.
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.
Tomita M (2001) Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol 19: 205–210.
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.
Edwards JS, Covert M, Palsson B (2002) Metabolic modelling of microbes: the flux-balance approach. Environ Microbiol 4: 133–140.
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.
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.
Oliveira AP, Nielsen J, Forster J (2005) Modeling Lactococcus lactisusing a genome-scale flux model. BMC Microbiol 5: 39.
Teusink B, Smid EJ (2006) Modelling strategies for the industrial exploitation of lactic acid bacteria. Nat Rev Microbiol 4: 46–56.
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.
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.
Christensen B, Gombert AK, Nielsen J (2002) Analysis of flux estimates based on 13C-labelling experiments. Eur J Biochem 269: 2795–2800.
Edwards JS, Ibarra RU, Palsson BO (2001) In silico predictions of Escherichia colimetabolic capabilities are consistent with experimental data. Nat Biotechnol 19: 125–130.
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.
Edwards JS, Palsson BO (2000) Metabolic flux balance analysis and the in silico analysis of Escherichia coliK-12 gene deletions. BMC Bioinformatics 1: 1.
Edwards JS, Ramakrishna R, Palsson BO (2002) Characterizing the metabolic phenotype: a phenotype phase plane analysis. Biotechnol Bioeng 77: 27–36.
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.
Forster J, Famili I, Palsson BO et al (2003) Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. Omics 7: 193–202.
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.
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.
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.
Bonarius HPJ, Schmid G, Tramper J (1997) Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. Trends Biotechnol 15: 308–314.
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.
Klamt S, Saez-Rodriguez J, Gilles ED (2007) Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol 1: 2.
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.
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.
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.
Kitano H, Funahashi A, Matsuoka Y et al (2005) Using process diagrams for the graphical representation of biological networks. Nat Biotechnol 23: 961–966.
Funahashi A, Tanimura N, Morohashi M et al (2003) CellDesigner: a process diagram editor for gene-regulatory and biochemical networks. BIOSILICO 1: 159–162.
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.
Zamboni N, Fischer E, Sauer U (2005) FiatFlux – a software for metabolic flux analysis from 13C-glucose experiments. BMC Bioinformatics 6: 209.
Wiechert W, Mollney M, Petersen S et al (2001) A universal framework for 13C metabolic flux analysis. Metab Eng 3: 265–283.
Quek LE, Wittmann C, Nielsen LK et al (2009) OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis. Microb Cell Fact 8: 25.
Pfeiffer T, Sanchez-Valdenebro I, Nuno JC et al (1999) METATOOL: for studying metabolic networks. Bioinformatics 15: 251–257.
Schwarz R, Liang C, Kaleta C et al (2007) Integrated network reconstruction, visualization and analysis using YANAsquare. BMC Bioinformatics 8: 313.
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.
Bell SL, Palsson BO (2005) Expa: a program for calculating extreme pathways in biochemical reaction networks. Bioinformatics 21: 1739–1740.
Acknowledgments
We are grateful to The Netherlands Vaccine Institute (NVI) for supporting this work.
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Baart, G.J.E., Martens, D.E. (2012). Genome-Scale Metabolic Models: Reconstruction and Analysis. In: Christodoulides, M. (eds) Neisseria meningitidis. Methods in Molecular Biology, vol 799. Humana, Totowa, NJ. https://doi.org/10.1007/978-1-61779-346-2_7
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