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Genome-Scale Metabolic Network Reconstruction

  • Marco FondiEmail author
  • Pietro Liò
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1231)

Abstract

Bacterial metabolism is an important source of novel products/processes for everyday life and strong efforts are being undertaken to discover and exploit new usable substances of microbial origin. Computational modeling and in silico simulations are powerful tools in this context since they allow the exploration and a deeper understanding of bacterial metabolic circuits. Many approaches exist to quantitatively simulate chemical reaction fluxes within the whole microbial metabolism and, regardless of the technique of choice, metabolic model reconstruction is the first step in every modeling pipeline. Reconstructing a metabolic network consists in drafting the list of the biochemical reactions that an organism can carry out together with information on cellular boundaries, a biomass assembly reaction, and exchange fluxes with the external environment. Building up models able to represent the different functional cellular states is universally recognized as a tricky task that requires intensive manual effort and much additional information besides genome sequence. In this chapter we present a general protocol for metabolic reconstruction in bacteria and the main challenges encountered during this process.

Key words

Metabolic model reconstruction Flux balance analysis Metabolic modeling 

References

  1. 1.
    Downs DM (2003) Genomics and bacterial metabolism. Curr Issues Mol Biol 5(1):17–25PubMedGoogle Scholar
  2. 2.
    Beloqui A, de Maria PD, Golyshin PN, Ferrer M (2008) Recent trends in industrial microbiology. Curr Opin Microbiol 11(3):240–248CrossRefPubMedGoogle Scholar
  3. 3.
    Zou W et al (2012) Reconstruction and analysis of a genome-scale metabolic model of the vitamin C producing industrial strain Ketogulonicigenium vulgare WSH-001. J Biotechnol 161(1):42–48CrossRefPubMedGoogle Scholar
  4. 4.
    Garcia-Ochoa F, Santos VE, Casas JA, Gomez E (2000) Xanthan gum: production, recovery, and properties. Biotechnol Adv 18(7):549–579CrossRefPubMedGoogle Scholar
  5. 5.
    George HA, Johnson JL, Moore WE, Holdeman LV, Chen JS (1983) Acetone, isopropanol, and butanol production by Clostridium beijerinckii (syn. Clostridium butylicum) and Clostridium aurantibutyricum. Appl Environ Microbiol 45(3):1160–1163PubMedPubMedCentralGoogle Scholar
  6. 6.
    Lee SJ et al (2005) Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. (Translated from eng). Appl Environ Microbiol 71(12):7880–7887CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Varma A, Palsson BO (1994) Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 60(10):3724–3731PubMedPubMedCentralGoogle Scholar
  8. 8.
    Oberhardt MA, Palsson BO, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5:320 (in eng)CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28(3):245–248CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Oberhardt MA, Chavali AK, Papin JA (2009) Flux balance analysis: interrogating genome-scale metabolic networks. Methods Mol Biol 500:61–80CrossRefPubMedGoogle Scholar
  11. 11.
    Fang X, Wallqvist A, Reifman J (2012) Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia. PLoS Comput Biol 8(9):e1002688CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Park JM, Kim TY, Lee SY (2009) Constraints-based genome-scale metabolic simulation for systems metabolic engineering. Biotechnol Adv 27(6):979–988 (in eng)CrossRefPubMedGoogle Scholar
  13. 13.
    Maarleveld TR, Khandelwal RA, Olivier BG, Teusink B, Bruggeman FJ (2013) Basic concepts and principles of stoichiometric modeling of metabolic networks. Biotechnol J 8(9):997–1008CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Moura M, Broadbelt L, Tyo K (2013) Computational tools for guided discovery and engineering of metabolic pathways. Methods Mol Biol 985:123–147CrossRefPubMedGoogle Scholar
  15. 15.
    Copeland WB et al (2012) Computational tools for metabolic engineering. Metab Eng 14(3):270–280CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Durot M, Bourguignon PY, Schachter V (2009) Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol Rev 33(1):164–190 (in eng)CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Aziz RK et al (2008) The RAST Server: rapid annotations using subsystems technology. (Translated from eng). BMC Genomics 9:75CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Schneider J et al (2010) CARMEN - comparative analysis and in silico reconstruction of organism-specific MEtabolic networks. Genet Mol Res 9(3):1660–1672CrossRefPubMedGoogle Scholar
  19. 19.
    Feng X, Xu Y, Chen Y, Tang YJ (2012) MicrobesFlux: a web platform for drafting metabolic models from the KEGG database. BMC Syst Biol 6:94CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Karp PD et al (2010) Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief Bioinform 11(1):40–79CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Reyes R et al (2012) Automation on the generation of genome-scale metabolic models. (Translated from eng). J Comput Biol 19(12):1295–1306CrossRefPubMedGoogle Scholar
  22. 22.
    Thiele I, Palsson BO (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5(1):93–121CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Thiele I, Palsson BO (2010) Reconstruction annotation jamborees: a community approach to systems biology. Mol Syst Biol 6:361CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Dandekar T, Fieselmann A, Majeed S, Ahmed Z (2012) Software applications toward quantitative metabolic flux analysis and modeling. Brief Bioinform 15:91CrossRefPubMedGoogle Scholar
  25. 25.
    Henry CS et al (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. (Translated from eng). Nat Biotechnol 28(9):977–982CrossRefPubMedGoogle Scholar
  26. 26.
    Kanehisa M (2002) The KEGG database. Novartis Found Symp 247:91–101, discussion 101–103, 119–128, 244–152CrossRefPubMedGoogle Scholar
  27. 27.
    Karp PD, Paley S (1996) Integrated access to metabolic and genomic data. J Comput Biol 3(1):191–212 (in eng)CrossRefPubMedGoogle Scholar
  28. 28.
    Karp PD, Paley S, Romero P (2002) The pathway tools software. Bioinformatics 18(Suppl 1):S225–S232CrossRefPubMedGoogle Scholar
  29. 29.
    Caspi R et al (2012) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 40(Database issue):D742–D753CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Karp PD, Riley M, Paley SM, Pellegrini-Toole A (2002) The MetaCyc database. Nucleic Acids Res 30(1):59–61CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Altman T, Travers M, Kothari A, Caspi R, Karp PD (2013) A systematic comparison of the MetaCyc and KEGG pathway databases. BMC Bioinform 14(1):112 (in Eng)CrossRefGoogle Scholar
  32. 32.
    Hyland C, Pinney JW, McConkey GA, Westhead DR (2006) metaSHARK: a WWW platform for interactive exploration of metabolic networks. Nucleic Acids Res 34(Web Server issue):W725–W728CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Pinney JW, Shirley MW, McConkey GA, Westhead DR (2005) metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella. Nucleic Acids Res 33(4):1399–1409CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Liao YC, Tsai MH, Chen FC, Hsiung CA (2012) GEMSiRV: a software platform for GEnome-scale metabolic model simulation, reconstruction and visualization. Bioinformatics 28(13):1752–1758CrossRefPubMedGoogle Scholar
  35. 35.
    Agren R et al (2013) The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput Biol 9(3):e1002980CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Swainston N, Smallbone K, Mendes P, Kell D, Paton N (2011) The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks. J Integr Bioinform 8(2):186PubMedGoogle Scholar
  37. 37.
    Hucka M et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4):524–531CrossRefPubMedGoogle Scholar
  38. 38.
    Bray T, Paoli J, Sperberg-McQueen CM (1998) Extensible markup language (XML) 1.0. Available from: http://www.w3.org/TR/1998/REC-xml-19980210
  39. 39.
    Feist AM et al (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 3:121CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Schellenberger J et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6(9):1290–1307CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Ebrahim A, Lerman JA, Palsson BO, Hyduke DR (2013) COBRApy: COnstraints-based reconstruction and analysis for python. BMC Syst Biol 7:74CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Bornstein BJ, Keating SM, Jouraku A, Hucka M (2008) LibSBML: an API library for SBML. Bioinformatics 24(6):880–881CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Karp PD et al (2000) The EcoCyc and MetaCyc databases. Nucleic Acids Res 28(1):56–59CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Oberhardt MA, Puchalka J, Fryer KE, Martins dos Santos VA, Papin JA (2008) Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol 190(8):2790–2803CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Fang K et al (2011) Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction. BMC Syst Biol 5:83CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Oberhardt MA, Puchalka J, Martins dos Santos VA, Papin JA (2011) Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis. PLoS Comput Biol 7(3):e1001116CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Saier MH Jr, Tran CV, Barabote RD (2006) TCDB: the Transporter Classification Database for membrane transport protein analyses and information. Nucleic Acids Res 34(Database issue):D181–D186CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Mahadevan R, Edwards JS, Doyle FJ 3rd (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 83(3):1331–1340 (in eng)CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Lisha KP, Sarkar D (2014) Dynamic flux balance analysis of batch fermentation: effect of genetic manipulations on ethanol production. Bioprocess Biosyst Eng 37(4):617–627CrossRefPubMedGoogle Scholar
  50. 50.
    Overbeek R et al (2005) The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 33(17):5691–5702CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Boele J, Olivier BG, Teusink B (2012) FAME, the flux analysis and modeling environment. BMC Syst Biol 6:8CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of BiologyUniversity of FlorenceFlorenceItaly
  2. 2.Computer LaboratoryUniversity of CambridgeCambridgeUK

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