Model-Guided Identification of Gene Deletion Targets for Metabolic Engineering in Saccharomyces cerevisiae

  • Ana Rita Brochado
  • Kiran Raosaheb PatilEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1152)


Identification of metabolic engineering strategies for rerouting intracellular fluxes towards a desired product is often a challenging task owing to the topological and regulatory complexity of metabolic networks. Genome-scale metabolic models help tackling this complexity through systematic consideration of mass balance and reaction directionality constraints over the entire network. Here, we describe how genome-scale metabolic models can be used for identifying gene deletion targets leading to increased production of the desired product. Vanillin production in Saccharomyces cerevisiae is used as a case study throughout this chapter.

Key words

Metabolic engineering Genome-scale metabolic reconstructions Flux balance analysis OptGene Minimization of metabolic adjustment Minimization of metabolite balance 


  1. 1.
    Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121CrossRefGoogle Scholar
  2. 2.
    Schellenberger J, Park JO, Conrad TM, Palsson BØ (2010) BiGG, a biochemical genetic and genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 11:213CrossRefGoogle Scholar
  3. 3.
    Oberhardt MA, Palsson BØ, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5:320CrossRefGoogle Scholar
  4. 4.
    Usaite R, Jewett MC, Oliveira AP et al (2009) Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator. Mol Syst Biol 5:319CrossRefGoogle Scholar
  5. 5.
    Patil KR, Nielsen J (2005) Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci U S A 102:2685–2689CrossRefGoogle Scholar
  6. 6.
    Covert MW, Knight EM, Reed JL et al (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature 429:92–96CrossRefGoogle Scholar
  7. 7.
    Kim TY, Sohn SB, Kim YB et al (2011) Recent advances in reconstruction and applications of genome-scale metabolic models. Curr Opin Biotechnol 23:1–7CrossRefGoogle Scholar
  8. 8.
    Osterlund T, Nookaew I, Nielsen J (2011) Fifteen years of large scale metabolic modeling of yeast, developments and impacts. Biotechnol Adv 30:979–988CrossRefGoogle Scholar
  9. 9.
    Stephanopoulos GN, Aristidou AA, Nielsen J (1998) Metabolic engineering, 1st edn. Academic, San Diego, CAGoogle Scholar
  10. 10.
    Hong K-K, Nielsen J (2012) Metabolic engineering of Saccharomyces cerevisiae, a key cell factory platform for future biorefineries. Cell Mol Life Sci 69:2671–2690CrossRefGoogle Scholar
  11. 11.
    Patil KR, Akesson M, Nielsen J (2004) Use of genome-scale microbial models for metabolic engineering. Curr Opin Biotechnol 15:64–69CrossRefGoogle Scholar
  12. 12.
    Bailey JE (1991) Toward a science of metabolic engineering. Science 252:1668–1675CrossRefGoogle Scholar
  13. 13.
    Varma A, Palsson BØ (1993) Metabolic capabilities of Escherichia coli. I. Synthesis of biosynthetic precursors and cofactors. J Theor Biol 165:477–502CrossRefGoogle Scholar
  14. 14.
    Varma A, Palsson BØ (1994) Metabolic flux balancing, basic concepts scientific and practical use. Nat Biotechnol 12:994–998CrossRefGoogle Scholar
  15. 15.
    Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14:491–496CrossRefGoogle Scholar
  16. 16.
    Edwards JS, Ibarra RU, Palsson BØ (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19:125–130CrossRefGoogle Scholar
  17. 17.
    Famili I, Förster J, Nielsen J, Palsson BØ (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: 13134–13139CrossRefGoogle Scholar
  18. 18.
    Zomorrodi AR, Suthers PF, Ranganathan S, Maranas CD (2012) Mathematical optimization applications in metabolic networks. Metab Eng 14:672–686CrossRefGoogle Scholar
  19. 19.
    Patil KR, Rocha I, Förster J, Nielsen J (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6:308CrossRefGoogle Scholar
  20. 20.
    Burgard AP, Pharkya P, Maranas CD (2003) Optknock, a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84:647–657CrossRefGoogle Scholar
  21. 21.
    Asadollahi M, Maury J, Patil KR et al (2009) Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. Metab Eng 11: 328–334CrossRefGoogle Scholar
  22. 22.
    Brochado AR, Matos C, Møller BL et al (2010) Improved vanillin production in baker’s yeast through in silico design. Microb Cell Fact 9:84CrossRefGoogle Scholar
  23. 23.
    Förster J, Famili I, Fu P et al (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13: 244–253CrossRefGoogle Scholar
  24. 24.
    Kuepfer L, Sauer U, Blank LM (2005) Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome Res 15:1421–1430CrossRefGoogle Scholar
  25. 25.
    Mo ML, Palsson BØ, Herrgård MJ (2009) Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 3:37CrossRefGoogle Scholar
  26. 26.
    Zomorrodi AR, Maranas CD (2010) Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data. BMC Syst Biol 4:178CrossRefGoogle Scholar
  27. 27.
    Heavner BD, Smallbone K, Barker B et al (2012) Yeast 5—an expanded reconstruction of the Saccharomyces cerevisiae metabolic network. BMC Syst Biol 6:55CrossRefGoogle Scholar
  28. 28.
    Kumar VS, Dasika MS, Maranas CD (2007) Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8:212CrossRefGoogle Scholar
  29. 29.
    Kumar VS, Maranas CD (2009) GrowMatch, an automated method for reconciling in silico/in vivo growth predictions. PLoS Comput Biol 5:e1000308CrossRefGoogle Scholar
  30. 30.
    Guijarro JM, Lagunas R (1984) Saccharomyces cerevisiae does not accumulate ethanol against concentration gradient. J Bacteriol 160: 874–878Google Scholar
  31. 31.
    Ibarra RU, Edwards JS, Palsson BØ (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420:20–23CrossRefGoogle Scholar
  32. 32.
    Segrè D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99:15112–15117CrossRefGoogle Scholar
  33. 33.
    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 102:7695–7700CrossRefGoogle Scholar
  34. 34.
    Brochado AR, Andrejev S, Maranas CD, Patil KR (2012) Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks. PLoS Comput Biol 8:e1002758CrossRefGoogle Scholar
  35. 35.
    Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3:119CrossRefGoogle Scholar
  36. 36.
    Sauer U (2006) Metabolic networks in motion, 13C-based flux analysis. Mol Syst Biol 2:62CrossRefGoogle Scholar
  37. 37.
    Reed JL (2012) Shrinking the metabolic solution space using experimental datasets. PLoS Comput Biol 8:e1002662CrossRefGoogle Scholar
  38. 38.
    Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5:264–276CrossRefGoogle Scholar
  39. 39.
    Rocha I, Maia P, Evangelista P et al (2010) OptFlux, an open-source software platform for in silico metabolic engineering. BMC Syst Biol 4:45CrossRefGoogle Scholar
  40. 40.
    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: 1290–1307CrossRefGoogle Scholar
  41. 41.
    Cvijovic M, Olivares-Hernández R, Agren R et al (2010) BioMet Toolbox, genome-wide analysis of metabolism. Nucleic Acids Res 38:W144–W149CrossRefGoogle Scholar
  42. 42.
    Tepper N, Shlomi T (2010) Predicting metabolic engineering knockout strategies for chemical production, accounting for competing pathways. Bioinformatics 26:536–543CrossRefGoogle Scholar
  43. 43.
    Feist AM, Zielinski CD, Orth JD et al (2010) Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metab Eng 12: 173–186CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2014

Authors and Affiliations

  1. 1.Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
  2. 2.Stuctural and Computational BiologyEuropean Molecular Biology LaboratoryHeidelbergGermany

Personalised recommendations