Computational Prediction of Essential Metabolic Genes Using Constraint-Based Approaches

  • Georg BaslerEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1279)


In this chapter, we describe the application of constraint-based modeling to predict the impact of gene deletions on a metabolic phenotype. The metabolic reactions taking place inside cells form large networks, which have been reconstructed at a genome-scale for several organisms at increasing levels of detail. By integrating mathematical modeling techniques with biochemical principles, constraint-based approaches enable predictions of metabolite fluxes and growth under specific environmental conditions or for genetically modified microorganisms. Similar to the experimental knockout of a gene, predicting the essentiality of a metabolic gene for a phenotype further allows to generate hypotheses on its biological function and design of genetic engineering strategies for biotechnological applications. Here, we summarize the principles of constraint-based approaches and provide a detailed description of the procedure to predict the essentiality of metabolic genes with respect to a specific metabolic function. We exemplify the approach by predicting the essentiality of reactions in the citric acid cycle for the production of glucose from fatty acids.

Key words

Genome-scale metabolic networks Gene essentiality Metabolic network analysis Constraint-based approaches Flux balance analysis TCA cycle Glyoxylate cycle 



I thank Tino Krell and Juan Luis Ramos for critical reading of the manuscript. This research was supported by a Marie Curie Intra-European Fellowship within the 7th European Community Framework Programme.


  1. 1.
    Feist AM, Herrgård MJ, Thiele I et al (2009) Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 7:129–143PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Henry CS, DeJongh M, Best AA et al (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28:977–982PubMedCrossRefGoogle Scholar
  3. 3.
    Oliveira AP, Nielsen J, Förster J (2005) Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol 5:39PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Thiele I, Vo TD, Price ND et al (2005) Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single- and double-deletion mutants. J Bacteriol 187:5818–5830PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Oh Y-K, Palsson BØ, Park SM et al (2007) Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem 282:28791–28799PubMedCrossRefGoogle Scholar
  6. 6.
    Nogales J, Palsson BØ, Thiele I (2008) A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Syst Biol 2:79PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Oberhardt MA, Puchałka J, Fryer KE et al (2008) Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol 190:2790–2803PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Quek L-E, Nielsen LK (2008) On the reconstruction of the Mus musculus genome-scale metabolic network model. Genome Inform 21:89–100PubMedCrossRefGoogle Scholar
  9. 9.
    Plata G, Hsiao T-L, Olszewski KL et al (2010) Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network. Mol Syst Biol 6:408PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Chang RL, Ghamsari L, Manichaikul A et al (2011) Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. Mol Syst Biol 7:518PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Milne CB, Eddy JA, Raju R et al (2011) Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052. BMC Syst Biol 5:130PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Orth JD, Conrad TM, Na J et al (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011. Mol Syst Biol 7:535PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    Heavner BD, Smallbone K, Barker B et al (2012) Yeast 5—an expanded reconstruction of the Saccharomyces cerevisiae metabolic network. BMC Syst Biol 6:55PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Thiele I, Swainston N, Fleming RMT et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419–425PubMedCrossRefGoogle Scholar
  15. 15.
    Wodke JAH, Puchałka J, Lluch-Senar M et al (2013) Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling. Mol Syst Biol 9:653PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Arnold A, Nikoloski Z (2014) Bottom-up metabolic reconstruction of Arabidopsis and its application to determining the metabolic costs of enzyme production. Plant Physiol 165:1380–1391PubMedCrossRefGoogle Scholar
  17. 17.
    Teusink B, Passarge J, Reijenga CA et al (2000) Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem 267:5313–5329PubMedCrossRefGoogle Scholar
  18. 18.
    Reddy VN, Mavrovouniotis ML, Liebman MN (1993) Petri net representations in metabolic pathways. Proc Int Conf Intell Syst Mol Biol 1:328–336PubMedGoogle Scholar
  19. 19.
    Schuster S, Hilgetag C (1994) On elementary flux modes in biochemical reaction systems at steady state. J Biol Syst 2:165–182CrossRefGoogle Scholar
  20. 20.
    Schilling CH, Letscher D, Palsson BØ (2000) Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J Theor Biol 203:229–248PubMedCrossRefGoogle Scholar
  21. 21.
    Visser D, Heijnen JJ (2003) Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics. Metab Eng 5:164–176PubMedCrossRefGoogle Scholar
  22. 22.
    Famili I, Mahadevan R, Palsson BØ (2005) k-Cone analysis: determining all candidate values for kinetic parameters on a network scale. Biophys J 88:1616–1625PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Steuer R, Gross T, Selbig J et al (2006) Structural kinetic modeling of metabolic networks. Proc Natl Acad Sci U S A 103:11868–11873PubMedCentralPubMedCrossRefGoogle Scholar
  24. 24.
    Conradi C, Flockerzi D, Raisch J et al (2007) Subnetwork analysis reveals dynamic features of complex (bio)chemical networks. Proc Natl Acad Sci U S A 104:19175–19180PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Ederer M, Gilles ED (2007) Thermodynamically feasible kinetic models of reaction networks. Biophys J 92:1846–1857PubMedCentralPubMedCrossRefGoogle Scholar
  26. 26.
    Bulik S, Grimbs S, Huthmacher C et al (2009) Kinetic hybrid models composed of mechanistic and simplified enzymatic rate laws—a promising method for speeding up the kinetic modelling of complex metabolic networks. FEBS J 276:410–424PubMedCrossRefGoogle Scholar
  27. 27.
    Jamshidi N, Palsson BØ (2010) Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models. Biophys J 98:175–185PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Varma A, Palsson BØ (1994) Metabolic flux balancing: basic concepts, scientific and practical use. Nat Biotechnol 12:994–998CrossRefGoogle Scholar
  29. 29.
    Edwards JS, Ibarra RU, Palsson BØ (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19:125–130PubMedCrossRefGoogle Scholar
  30. 30.
    Famili I, Forster J, Nielsen J et al (2003) Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc Natl Acad Sci U S A 100:13134–13139PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    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
  32. 32.
    Pharkya P, Burgard AP, Maranas CD (2004) OptStrain: a computational framework for redesign of microbial production systems. Genome Res 14:2367–2376PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Melzer G, Esfandabadi ME, Franco-Lara E et al (2009) Flux Design: in silico design of cell factories based on correlation of pathway fluxes to desired properties. BMC Syst Biol 3:120PubMedCentralPubMedCrossRefGoogle Scholar
  34. 34.
    Hädicke O, Klamt S (2010) CASOP: a computational approach for strain optimization aiming at high productivity. J Biotechnol 147:88–101PubMedCrossRefGoogle Scholar
  35. 35.
    Yang L, Cluett WR, Mahadevan R (2011) EMILiO: a fast algorithm for genome-scale strain design. Metab Eng 13:272–281PubMedCrossRefGoogle Scholar
  36. 36.
    Driouch H, Melzer G, Wittmann C (2012) Integration of in vivo and in silico metabolic fluxes for improvement of recombinant protein production. Metab Eng 14:47–58PubMedCrossRefGoogle Scholar
  37. 37.
    Larhlimi A, Basler G, Grimbs S et al (2012) Stoichiometric capacitance reveals the theoretical capabilities of metabolic networks. Bioinformatics 28:i502–i508PubMedCentralPubMedCrossRefGoogle Scholar
  38. 38.
    Park JH, Lee KH, Kim TY et al (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 104:7797–7802PubMedCentralPubMedCrossRefGoogle Scholar
  39. 39.
    Asadollahi MA, Maury J, Patil KR et al (2009) Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. Metab Eng 11:328–334PubMedCrossRefGoogle Scholar
  40. 40.
    Choi HS, Lee SY, Kim TY et al (2010) In silico identification of gene amplification targets for improvement of lycopene production. Appl Environ Microbiol 76:3097–3105PubMedCentralPubMedCrossRefGoogle Scholar
  41. 41.
    Sohn SB, Kim TY, Park JM et al (2010) In silico genome-scale metabolic analysis of Pseudomonas putida KT2440 for polyhydroxyalkanoate synthesis, degradation of aromatics and anaerobic survival. Biotechnol J 5:739–750PubMedCrossRefGoogle Scholar
  42. 42.
    Poblete-Castro I, Binger D, Rodrigues A et al (2013) In-silico-driven metabolic engineering of Pseudomonas putida for enhanced production of poly-hydroxyalkanoates. Metab Eng 15:113–123PubMedCrossRefGoogle Scholar
  43. 43.
    Kleessen S, Nikoloski Z (2012) Dynamic regulatory on/off minimization for biological systems under internal temporal perturbations. BMC Syst Biol 6:16PubMedCentralPubMedCrossRefGoogle Scholar
  44. 44.
    Covert MW, Palsson BØ (2003) Constraints-based models: regulation of gene expression reduces the steady-state solution space. J Theor Biol 221:309–325PubMedCrossRefGoogle Scholar
  45. 45.
    Shlomi T, Eisenberg Y, Sharan R et al (2007) A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol Syst Biol 3:101PubMedCentralPubMedCrossRefGoogle Scholar
  46. 46.
    O’Brien EJ, Lerman JA, Chang RL et al (2013) Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 9:693PubMedCentralPubMedGoogle Scholar
  47. 47.
    Gianchandani EP, Chavali AK, Papin JA (2010) The application of flux balance analysis in systems biology. Wiley Interdiscip Rev Syst Biol Med 2:372–382PubMedCrossRefGoogle Scholar
  48. 48.
    Schellenberger J, Que R, Fleming RMT et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6:1290–1307PubMedCentralPubMedCrossRefGoogle Scholar
  49. 49.
    Schellenberger J, Park JO, Conrad TM et al (2010) BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 11:213PubMedCentralPubMedCrossRefGoogle Scholar
  50. 50.
    Chelliah V, Laibe C, Le Novère N (2013) BioModels Database: a repository of mathematical models of biological processes. Methods Mol Biol 1021:189–199PubMedCrossRefGoogle Scholar
  51. 51.
    Herrgård MJ, Swainston N, Dobson P et al (2008) A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 26:1155–1160PubMedCentralPubMedCrossRefGoogle Scholar
  52. 52.
    Mintz-Oron S, Meir S, Malitsky S et al (2012) Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proc Natl Acad Sci U S A 109:339–344PubMedCentralPubMedCrossRefGoogle Scholar
  53. 53.
    Clarke BL (1988) Stoichiometric network analysis. Cell Biophys 12:237–253PubMedCrossRefGoogle Scholar
  54. 54.
    Heinrich R, Schuster S (1996) The regulation of cellular systems. Springer, New YorkCrossRefGoogle Scholar
  55. 55.
    Feist AM, Palsson BØ (2010) The biomass objective function. Curr Opin Microbiol 13:344–349PubMedCentralPubMedCrossRefGoogle Scholar
  56. 56.
    Papp B, Pál C, Hurst LD (2004) Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast. Nature 429:661–664PubMedCrossRefGoogle Scholar
  57. 57.
    Gianchandani EP, Oberhardt MA, Burgard AP et al (2008) Predicting biological system objectives de novo from internal state measurements. BMC Bioinformatics 9:43PubMedCentralPubMedCrossRefGoogle Scholar
  58. 58.
    Gruer MJ, Guest JR (1994) Two genetically-distinct and differentially-regulated aconitases (AcnA and AcnB) in Escherichia coli. Microbiology 140(Pt 10):2531–2541PubMedCrossRefGoogle Scholar
  59. 59.
    Buck D, Spencer ME, Guest JR (1985) Primary structure of the succinyl-CoA synthetase of Escherichia coli. Biochemistry 24:6245–6252PubMedCrossRefGoogle Scholar
  60. 60.
    Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121PubMedCentralPubMedCrossRefGoogle Scholar
  61. 61.
    Segrè D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99:15112–15117PubMedCentralPubMedCrossRefGoogle Scholar
  62. 62.
    Shlomi T, Berkman O, Ruppin E (2005) Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc Natl Acad Sci U S A 102:7695–7700PubMedCentralPubMedCrossRefGoogle Scholar
  63. 63.
    Kornberg HL, Krebs HA (1957) Synthesis of cell constituents from C2-units by a modified tricarboxylic acid cycle. Nature 179:988–991PubMedCrossRefGoogle Scholar
  64. 64.
    de Figueiredo LF, Schuster S, Kaleta C et al (2009) Can sugars be produced from fatty acids? A test case for pathway analysis tools. Bioinformatics 25:152–158PubMedCrossRefGoogle Scholar
  65. 65.
    Pramanik J, Keasling JD (1997) Stoichiometric model of Escherichia coli metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements. Biotechnol Bioeng 56:398–421PubMedCrossRefGoogle Scholar
  66. 66.
    Schaechter M, Maaloe O, Kjeldgaard NO (1958) Dependency on medium and temperature of cell size and chemical composition during balanced grown of Salmonella typhimurium. J Gen Microbiol 19:592–606PubMedCrossRefGoogle Scholar
  67. 67.
    Sriram G, González-Rivera O, Shanks JV (2006) Determination of biomass composition of Catharanthus roseus hairy roots for metabolic flux analysis. Biotechnol Prog 22:1659–1663PubMedGoogle Scholar
  68. 68.
    Poolman MG, Miguet L, Sweetlove LJ et al (2009) A genome-scale metabolic model of Arabidopsis and some of its properties. Plant Physiol 151:1570–1581PubMedCentralPubMedCrossRefGoogle Scholar
  69. 69.
    Żur I, Skoczowski A, Pieńkowski S et al (2002) Kinetics of 14C-labelled sucrose, myo-inositol and phosphatidylcholine uptake during induction and differentiation in Brassica napus callus culture. Acta Physiol Plant 24:11–17Google Scholar
  70. 70.
    Whiteside MD, Garcia MO, Treseder KK (2012) Amino acid uptake in arbuscular mycorrhizal plants. PLoS One 7:e47643PubMedCentralPubMedCrossRefGoogle Scholar
  71. 71.
    Covert MW, Schilling CH, Palsson BØ (2001) Regulation of gene expression in flux balance models of metabolism. J Theor Biol 213:73–88PubMedCrossRefGoogle Scholar
  72. 72.
    Folger O, Jerby L, Frezza C et al (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:501PubMedCentralPubMedCrossRefGoogle Scholar
  73. 73.
    Mavrovouniotis ML (1991) Estimation of standard Gibbs energy changes of biotransformations. J Biol Chem 266:14440–14445PubMedGoogle Scholar
  74. 74.
    Tanaka M, Okuno Y, Yamada T et al (2003) Extraction of a thermodynamic property for biochemical reactions in the metabolic pathway. Genome Inform 14:370–371Google Scholar
  75. 75.
    Henry CS, Jankowski MD, Broadbelt LJ et al (2006) Genome-scale thermodynamic analysis of Escherichia coli metabolism. Biophys J 90:1453–1461PubMedCentralPubMedCrossRefGoogle Scholar
  76. 76.
    Hoppe A, Hoffmann S, Holzhütter H-G (2007) Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks. BMC Syst Biol 1:23PubMedCentralPubMedCrossRefGoogle Scholar
  77. 77.
    Henry CS, Broadbelt LJ, Hatzimanikatis V (2007) Thermodynamics-based metabolic flux analysis. Biophys J 92:1792–1805PubMedCentralPubMedCrossRefGoogle Scholar
  78. 78.
    Maskow T, von Stockar U (2005) How reliable are thermodynamic feasibility statements of biochemical pathways? Biotechnol Bioeng 92:223–230PubMedCrossRefGoogle Scholar
  79. 79.
    Vojinović V, von Stockar U (2009) Influence of uncertainties in pH, pMg, activity coefficients, metabolite concentrations, and other factors on the analysis of the thermodynamic feasibility of metabolic pathways. Biotechnol Bioeng 103:780–795PubMedCrossRefGoogle Scholar
  80. 80.
    Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5:264–276PubMedCrossRefGoogle Scholar
  81. 81.
    Reed JL, Palsson BØ (2004) Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res 14:1797–1805PubMedCentralPubMedCrossRefGoogle Scholar
  82. 82.
    Lee S, Phalakornkule C, Domach MM et al (2000) Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Comput Chem Eng 24:711–716CrossRefGoogle Scholar
  83. 83.
    Burgard AP, Nikolaev EV, Schilling CH et al (2004) Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res 14:301–312PubMedCentralPubMedCrossRefGoogle Scholar
  84. 84.
    Larhlimi A, David L, Selbig J et al (2012) F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks. BMC Bioinformatics 13:57PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Environmental Protection, Estación Experimental del ZaidínConsejo Superior de Investigaciones Científicas (CSIC)GranadaSpain

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