Development of Constraint-Based System-Level Models of Microbial Metabolism

  • Ali Navid
Part of the Methods in Molecular Biology book series (MIMB, volume 881)


Genome-scale models of metabolism are valuable tools for using genomic information to predict microbial phenotypes. System-level mathematical models of metabolic networks have been developed for a number of microbes and have been used to gain new insights into the biochemical conversions that occur within organisms and permit their survival and proliferation. Utilizing these models, computational biologists can (1) examine network structures, (2) predict metabolic capabilities and resolve unexplained experimental observations, (3) generate and test new hypotheses, (4) assess the nutritional requirements of the organism and approximate its environmental niche, (5) identify missing enzymatic functions in the annotated genome, and (6) engineer desired metabolic capabilities in model organisms. This chapter details the protocol for developing genome-scale models of metabolism in microbes as well as tips for accelerating the model building process.

Key words

Systems biology Genome-scale models Constraint-based analysis FBA Metabolic networks 



This work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes. LLNL-BOOK-491430.


  1. 1.
    Forster J, Famili I, Fu P, Palsson BO, Nielsen J (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13:244–253PubMedCrossRefGoogle Scholar
  2. 2.
    Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BO (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 3:121PubMedCrossRefGoogle Scholar
  3. 3.
    Thiele I, Vo TD, Price ND, Palsson BO (2005) Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single- and double-deletion mutants. J Bacteriol 187:5818–5830PubMedCrossRefGoogle Scholar
  4. 4.
    Chavali AK, Whittemore JD, Eddy JA, Williams KT, Papin JA (2008) Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major. Mol Syst Biol 4:177PubMedCrossRefGoogle Scholar
  5. 5.
    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 190:2790–2803PubMedCrossRefGoogle Scholar
  6. 6.
    Becker SA, Palsson BO (2005) Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC Microbiol 5:8PubMedCrossRefGoogle Scholar
  7. 7.
    Navid A, Almaas E (2009) Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001. Mol Biosyst 5:368–375PubMedCrossRefGoogle Scholar
  8. 8.
    Almaas E, Kovacs B, Vicsek T, Oltvai ZN, Barabasi AL (2004) Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature 427:839–843PubMedCrossRefGoogle Scholar
  9. 9.
    Almaas E (2007) Optimal flux patterns in cellular metabolic networks. Chaos 17:026107PubMedCrossRefGoogle Scholar
  10. 10.
    Almaas E, Oltvai ZN, Barabasi AL (2005) The activity reaction core and plasticity of metabolic networks. PLoS Comput Biol 1:e68PubMedCrossRefGoogle Scholar
  11. 11.
    Gagneur J, Jackson DB, Casari G (2003) Hierarchical analysis of dependency in metabolic networks. Bioinformatics 19:1027–1034PubMedCrossRefGoogle Scholar
  12. 12.
    Segre D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99:15112–15117PubMedCrossRefGoogle Scholar
  13. 13.
    Deutscher D, Meilijson I, Kupiec M, Ruppin E (2006) Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nat Genet 38:993–998PubMedCrossRefGoogle Scholar
  14. 14.
    Jamshidi N, Palsson BO (2006) Systems biology of SNPs. Mol Syst Biol 2:38PubMedCrossRefGoogle Scholar
  15. 15.
    Edwards JS, Palsson BO (2000) Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics 1:1PubMedCrossRefGoogle Scholar
  16. 16.
    Reed JL, Famili I, Thiele I, Palsson BO (2006) Towards multidimensional genome annotation. Nat Rev Genet 7:130–141PubMedCrossRefGoogle Scholar
  17. 17.
    Pal C, Papp B, Lercher MJ (2005) Adaptive evolution of bacterial metabolic networks by horizontal gene transfer. Nat Genet 37:1372–1375PubMedCrossRefGoogle Scholar
  18. 18.
    Pal C, Papp B, Lercher MJ (2005) Horizontal gene transfer depends on gene content of the host. Bioinformatics 21(suppl 2):ii222–ii223PubMedCrossRefGoogle Scholar
  19. 19.
    Pal C, Papp B, Lercher MJ, Csermely P, Oliver SG, Hurst LD (2006) Chance and necessity in the evolution of minimal metabolic networks. Nature 440:667–670PubMedCrossRefGoogle Scholar
  20. 20.
    Pharkya P, Burgard AP, Maranas CD (2003) Exploring the overproduction of amino acids using the bilevel optimization framework OptKnock. Biotechnol Bioeng 84:887–899PubMedCrossRefGoogle Scholar
  21. 21.
    Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84:647–657PubMedCrossRefGoogle Scholar
  22. 22.
    Pharkya P, Burgard AP, Maranas CD (2004) OptStrain: a computational framework for redesign of microbial production systems. Genome Res 14:2367–2376PubMedCrossRefGoogle Scholar
  23. 23.
    Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, Palsson BO (2005) In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol Bioeng 91:643–648PubMedCrossRefGoogle Scholar
  24. 24.
    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 U S A 104:7797–7802PubMedCrossRefGoogle Scholar
  25. 25.
    Oberhardt MA, Palsson BO, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5:320PubMedCrossRefGoogle Scholar
  26. 26.
    Notebaart RA, van Enckevort FH, Francke C, Siezen RJ, Teusink B (2006) Accelerating the reconstruction of genome-scale metabolic networks. BMC Bioinformatics 7:296PubMedCrossRefGoogle Scholar
  27. 27.
    DeJongh M, Formsma K, Boillot P, Gould J, Rycenga M, Best A (2007) Toward the automated generation of genome-scale metabolic networks in the SEED. BMC Bioinformatics 8:139PubMedCrossRefGoogle Scholar
  28. 28.
    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 28:977–982PubMedCrossRefGoogle Scholar
  29. 29.
    Varma A, Palsson BO (1994) Metabolic flux balancing: basic concepts, scientific and practical use. Nat Biotechnol 12:994–998CrossRefGoogle Scholar
  30. 30.
    Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28:245–248PubMedCrossRefGoogle Scholar
  31. 31.
    Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3:119PubMedCrossRefGoogle Scholar
  32. 32.
    Keseler IM, Collado-Vides J, Santos-Zavaleta A, Peralta-Gil M, Gama-Castro S, Muniz-Rascado L, Bonavides-Martinez C, Paley S, Krummenacker M, Altman T, Kaipa P, Spaulding A, Pacheco J, Latendresse M, Fulcher C, Sarker M, Shearer AG, Mackie A, Paulsen I, Gunsalus RP, Karp PD (2011) EcoCyc: a comprehensive database of Escherichia coli biology. Nucleic Acids Res 39:D583–D590PubMedCrossRefGoogle Scholar
  33. 33.
    Guldener U, Munsterkotter M, Kastenmuller G, Strack N, van Helden J, Lemer C, Richelles J, Wodak SJ, Garcia-Martinez J, Perez-Ortin JE, Michael H, Kaps A, Talla E, Dujon B, Andre B, Souciet JL, De Montigny J, Bon E, Gaillardin C, Mewes HW (2005) CYGD: the Comprehensive Yeast Genome Database. Nucleic Acids Res 33:D364–D368PubMedCrossRefGoogle Scholar
  34. 34.
    Markowitz VM, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, Ratner A, Anderson I, Lykidis A, Mavromatis K, Ivanova NN, Kyrpides NC (2010) The integrated microbial genomes system: an expanding comparative analysis resource. Nucleic Acids Res 38:D382–D390PubMedCrossRefGoogle Scholar
  35. 35.
    Maglott D, Ostell J, Pruitt KD, Tatusova T (2007) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res 35:D26–D31PubMedCrossRefGoogle Scholar
  36. 36.
    Peterson JD, Umayam LA, Dickinson T, Hickey EK, White O (2001) The comprehensive microbial resource. Nucleic Acids Res 29:123–125PubMedCrossRefGoogle Scholar
  37. 37.
    Karp PD, Paley SM, Krummenacker M, Latendresse M, Dale JM, Lee TJ, Kaipa P, Gilham F, Spaulding A, Popescu L, Altman T, Paulsen I, Keseler IM, Caspi R (2010) Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief Bioinform 11:40–79PubMedCrossRefGoogle Scholar
  38. 38.
    Lee DY, Yun H, Park S, Lee SY (2003) MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics 19:2144–2146PubMedCrossRefGoogle Scholar
  39. 39.
    Lee SY, Lee DY, Hong SH, Kim TY, Yun H, Oh YG, Park S (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 14:23–33PubMedGoogle Scholar
  40. 40.
    Hoppe A, Hoffmann S, Gerasch A, Gille C, Holzhutter HG (2011) FASIMU: flexible software for flux-balance computation series in large metabolic networks. BMC Bioinformatics 12:28PubMedCrossRefGoogle Scholar
  41. 41.
    Forth T, McConkey GA, Westhead DR (2010) MetNetMaker: a free and open-source tool for the creation of novel metabolic networks in SBML format. Bioinformatics 26:2352PubMedCrossRefGoogle Scholar
  42. 42.
    Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2:727–738PubMedCrossRefGoogle Scholar
  43. 43.
    Schomburg I, Chang A, Hofmann O, Ebeling C, Ehrentreich F, Schomburg D (2002) BRENDA: a resource for enzyme data and metabolic information. Trends Biochem Sci 27:54–56PubMedCrossRefGoogle Scholar
  44. 44.
    Chang A, Scheer M, Grote A, Schomburg I, Schomburg D (2009) BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009. Nucleic Acids Res 37:D588PubMedCrossRefGoogle Scholar
  45. 45.
    Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27PubMedCrossRefGoogle Scholar
  46. 46.
    Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res 32:D277PubMedCrossRefGoogle Scholar
  47. 47.
    Caspi R, Foerster H, Fulcher CA, Kaipa P, Krummenacker M, Latendresse M, Paley S, Rhee SY, Shearer AG, Tissier C, Walk TC, Zhang P, Karp PD (2008) The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res 36:D623–D631PubMedCrossRefGoogle Scholar
  48. 48.
    Ren Q, Kang KH, Paulsen IT (2004) TransportDB: a relational database of cellular membrane transport systems. Nucleic Acids Res 32:D284PubMedCrossRefGoogle Scholar
  49. 49.
    Ren Q, Chen K, Paulsen IT (2006) TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. Nucleic Acids Res 35:D274PubMedCrossRefGoogle Scholar
  50. 50.
    Alberty RA (1998) Calculation of standard transformed formation properties of biochemical reactants and standard apparent reduction potentials of half reactions. Arch Biochem Biophys 358:25–39PubMedCrossRefGoogle Scholar
  51. 51.
    Alberty RA (1998) Calculation of standard transformed Gibbs energies and standard transformed enthalpies of biochemical reactants. Arch Biochem Biophys 353:116–130PubMedCrossRefGoogle Scholar
  52. 52.
    Kummel A, Panke S, Heinemann M (2006) Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinformatics 7:512PubMedCrossRefGoogle Scholar
  53. 53.
    Mavrovouniotis ML (1990) Group contributions for estimating standard gibbs energies of formation of biochemical compounds in aqueous solution. Biotechnol Bioeng 36:1070–1082PubMedCrossRefGoogle Scholar
  54. 54.
    Jankowski MD, Henry CS, Broadbelt LJ, Hatzimanikatis V (2008) Group contribution method for thermodynamic analysis of complex metabolic networks. Biophys J 95:1487–1499PubMedCrossRefGoogle Scholar
  55. 55.
    Henry CS, Jankowski MD, Broadbelt LJ, Hatzimanikatis V (2006) Genome-scale thermodynamic analysis of Escherichia coli metabolism. Biophys J 90:1453–1461PubMedCrossRefGoogle Scholar
  56. 56.
    Henry CS, Broadbelt LJ, Hatzimanikatis V (2007) Thermodynamics-based metabolic flux analysis. Biophys J 92:1792–1805PubMedCrossRefGoogle Scholar
  57. 57.
    Tanaka M, Okuno Y, Yamada T, Goto S, Uemura S, Kanehisa M (2003) Extraction of a thermodynamic property for biochemical reactions in the metabolic pathway. Genome Inform 14:370–371Google Scholar
  58. 58.
    Parkhill J, Wren BW, Thomson NR, Titball RW, Holden MT, Prentice MB, Sebaihia M, James KD, Churcher C, Mungall KL, Baker S, Basham D, Bentley SD, Brooks K, Cerdeno-Tarraga AM, Chillingworth T, Cronin A, Davies RM, Davis P, Dougan G, Feltwell T, Hamlin N, Holroyd S, Jagels K, Karlyshev AV, Leather S, Moule S, Oyston PC, Quail M, Rutherford K, Simmonds M, Skelton J, Stevens K, Whitehead S, Barrell BG (2001) Genome sequence of Yersinia pestis, the causative agent of plague. Nature 413:523–527PubMedCrossRefGoogle Scholar
  59. 59.
    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 60:3724–3731PubMedGoogle Scholar
  60. 60.
    Neidhardt FC, Curtiss R III, Ingraham J, Lin E, Low K, Magasanik B, Reznikoff W, Riley M, Schaechter M, Umbarger H (1996) Escherichia coli and Salmonella: cellular and molecular biology, vol 2327. American Society for Microbiology, Washington, DCGoogle Scholar
  61. 61.
    Tekaia F, Yeramian E, Dujon B (2002) Amino acid composition of genomes, lifestyles of organisms, and evolutionary trends: a global picture with correspondence analysis. Gene 297:51–60PubMedCrossRefGoogle Scholar
  62. 62.
    Dumontier M, Michalickova K, Hogue C (2002) Species-specific protein sequence and fold optimizations. BMC Bioinformatics 3:39PubMedCrossRefGoogle Scholar
  63. 63.
    Feist AM, Palsson BO (2010) The biomass objective function. Curr Opin Microbiol 13:344–349PubMedCrossRefGoogle Scholar
  64. 64.
    Tang YJ, Martin HG, Myers S, Rodriguez S, Baidoo EEK, Keasling JD (2009) Advances in analysis of microbial metabolic fluxes via 13C isotopic labeling. Mass Spectrom Rev 28:362–375PubMedCrossRefGoogle Scholar
  65. 65.
    Fischer E, Zamboni N, Sauer U (2004) High-throughput metabolic flux analysis based on gas chromatography-mass spectrometry derived 13C constraints. Anal Biochem 325:308–316PubMedCrossRefGoogle Scholar
  66. 66.
    Sauer U (2006) Metabolic networks in motion: 13C-based flux analysis. Mol Syst Biol 2:62PubMedCrossRefGoogle Scholar
  67. 67.
    Stewart BJ, Navid A, Turteltaub KW, Bench G (2010) Yeast dynamic metabolic flux measurement in nutrient-rich media by HPLC and accelerator mass spectrometry. Anal Chem 82:9812–9817PubMedCrossRefGoogle Scholar
  68. 68.
    Green ML, Karp PD (2004) A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinformatics 5:76PubMedCrossRefGoogle Scholar
  69. 69.
    Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, Formsma K, Gerdes S, Glass EM, Kubal M (2008) The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9:75PubMedCrossRefGoogle Scholar
  70. 70.
    Tian W, Arakaki AK, Skolnick J (2004) EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference. Nucleic Acids Res 32:6226PubMedCrossRefGoogle Scholar
  71. 71.
    Price ND, Reed JL, Palsson BO (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2:886–897PubMedCrossRefGoogle Scholar
  72. 72.
    Feist AM, Palsson BO (2008) The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat Biotechnol 26:659–667PubMedCrossRefGoogle Scholar
  73. 73.
    Milne CB, Kim PJ, Eddy JA, Price ND (2009) Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology. Biotechnol J 4:1653–1670PubMedCrossRefGoogle Scholar
  74. 74.
    Liu L, Agren R, Bordel S, Nielsen J (2010) Use of genome-scale metabolic models for understanding microbial physiology. FEBS Lett 584:2556–2564PubMedCrossRefGoogle Scholar
  75. 75.
    Knorr AL, Jain R, Srivastava R (2007) Bayesian-based selection of metabolic objective functions. Bioinformatics 23:351–357PubMedCrossRefGoogle Scholar
  76. 76.
    Holzhutter HG (2004) The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur J Biochem 271:2905–2922PubMedCrossRefGoogle Scholar
  77. 77.
    Oliveira AP, Nielsen J, Förster J (2005) Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol 5:39PubMedCrossRefGoogle Scholar
  78. 78.
    Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14:491–496PubMedCrossRefGoogle Scholar
  79. 79.
    Krummenacker M, Paley S, Mueller L, Yan T, Karp PD (2005) Querying and computing with BioCyc databases. Bioinformatics 21:3454–3455PubMedCrossRefGoogle Scholar
  80. 80.
    Maglott D, Ostell J, Pruitt KD, Tatusova T (2011) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res 39:D52–D57PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Biosciences and Biotechnology Division, Physics and Life Sciences DirectorateLawrence Livermore National LaboratoryLivermoreUSA

Personalised recommendations