Skip to main content
SpringerLink
Log in

Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology

  • Protocol
  • First Online:
Yeast Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 759))

Abstract

One of the major aims of the nascent field of evolutionary systems biology is to test evolutionary hypotheses that are not only realistic from a population genetic point of view but also detailed in terms of molecular biology mechanisms. By providing a mapping between genotype and phenotype for hundreds of genes, genome-scale systems biology models of metabolic networks have already provided valuable insights into the evolution of metabolic gene contents and phenotypes of yeast and other microbial species. Here we review the recent use of these computational models to predict the fitness effect of mutations, genetic interactions, evolutionary outcomes, and to decipher the mechanisms of mutational robustness. While these studies have demonstrated that even simplified models of biochemical reaction networks can be highly informative for evolutionary analyses, they have also revealed the weakness of this modeling framework to quantitatively predict mutational effects, a challenge that needs to be addressed for future progress in evolutionary systems biology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Loewe, L. (2009) A framework for evolutionary systems biology. BMC Syst. Biol. 3, 27.

    Article  PubMed  Google Scholar 

  2. Endy, D., You, L., Yin, J., and Molineux, I. J. (2000) Computation, prediction, and experimental tests of fitness for bacteriophage T7 mutants with permuted genomes. Proc. Natl. Acad. Sci. USA 97, 5375–5380.

    Article  PubMed  CAS  Google Scholar 

  3. Papp, B., Pál, C., and Hurst, L. D. (2004) Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast. Nature 429, 661–664.

    Article  PubMed  CAS  Google Scholar 

  4. Notebaart, R. A., Kensche, P. R., Huynen, M. A., and Dutilh, B. E. (2009) Asymmetric relationships between proteins shape genome evolution. Genome Biol. 10, R19.

    Article  PubMed  Google Scholar 

  5. Pál, C., Papp, B., Lercher, M. J., Csermely, P., Oliver, S. G., and Hurst, L. D. (2006) Chance and necessity in the evolution of minimal metabolic networks. Nature 440, 667–670.

    Article  PubMed  Google Scholar 

  6. Loewe, L., and Hillston, J. (2008) The distribution of mutational effects on fitness in a simple circadian clock. Lect. Notes Bioinf. 5307, 156–175.

    Google Scholar 

  7. Teusink, B., Walsh, M. C., van Dam, K., and Westerhoff, H. V. (1998) The danger of metabolic pathways with turbo design. Trends Biochem. Sci. 23, 162–169.

    Article  PubMed  CAS  Google Scholar 

  8. Chen, K. C., Calzone, L., Csikasz-Nagy, A., Cross, F. R., Novak, B., and Tyson, J. J. (2004) Integrative analysis of cell cycle control in budding yeast. Mol. Biol. Cell 15, 3841–3862.

    Article  PubMed  CAS  Google Scholar 

  9. Christensen, T. S., Oliveira, A. P., and Nielsen, J. (2009) Reconstruction and logical modeling of glucose repression signaling pathways in Saccharomyces cerevisiae. BMC Syst. Biol. 3, 7.

    Article  PubMed  Google Scholar 

  10. Price, N. D., Reed, J. L., and Palsson, B. O. (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol. 2, 886–897.

    Article  PubMed  CAS  Google Scholar 

  11. Kauffman, K. J., Prakash, P., and Edwards, J. S. (2003) Advances in flux balance analysis. Curr. Opin. Biotechnol. 14, 491–496.

    Article  PubMed  CAS  Google Scholar 

  12. Poelwijk, F. J., Kiviet, D. J., Weinreich, D. M., and Tans, S. J. (2007) Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383–386.

    Article  PubMed  CAS  Google Scholar 

  13. Borenstein, E., Kupiec, M., Feldman, M. W., and Ruppin, E. (2008) Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proc. Natl. Acad. Sci. USA 105, 14482–14487.

    Article  PubMed  CAS  Google Scholar 

  14. Freilich, S., Kreimer, A., Borenstein, E., et al. (2009) Metabolic-network-driven analysis of bacterial ecological strategies. Genome Biol. 10, R61.

    Article  PubMed  Google Scholar 

  15. Harrison, R., Papp, B., Pal, C., Oliver, S. G., and Delneri, D. (2007) Plasticity of genetic interactions in metabolic networks of yeast. Proc. Natl. Acad. Sci. USA 104, 2307–2312.

    Article  PubMed  CAS  Google Scholar 

  16. Raman, K., Rajagopalan, P., and Chandra, N. (2005) Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs. PLoS Comput. Biol. 1, e46.

    Article  PubMed  Google Scholar 

  17. Lee, D. S., Burd, H., Liu, J., et al. (2009) Comparative genome-scale metabolic reconstruction and flux balance analysis of multiple Staphylococcus aureus genomes identify novel antimicrobial drug targets. J. Bacteriol. 191, 4015–4024.

    Article  PubMed  CAS  Google Scholar 

  18. Forster, J., Famili, I., Fu, P., Palsson, B. O., and Nielsen, J. (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13, 244–253.

    Article  PubMed  CAS  Google Scholar 

  19. Forster, J., Famili, I., Palsson, B. O., and Nielsen, J. (2003) Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. Omics 7, 193–202.

    Article  PubMed  Google Scholar 

  20. Duarte, N. C., Herrgard, M. J., and Palsson, B. O. (2004) Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res. 14, 1298–1309.

    Article  PubMed  CAS  Google Scholar 

  21. Kuepfer, L., Sauer, U., and Blank, L. M. (2005) Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome Res. 15, 1421–1430.

    Article  PubMed  CAS  Google Scholar 

  22. Snitkin, E. S., Dudley, A. M., Janse, D. M., Wong, K., Church, G. M., and Segre, D. (2008) Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions. Genome Biol. 9, R140.

    Article  PubMed  Google Scholar 

  23. Becker, S. A., and Palsson, B. O. (2008) Three factors underlying incorrect in silico predictions of essential metabolic genes. BMC Syst. Biol. 2, 14.

    Article  PubMed  Google Scholar 

  24. Segrè, D., Vitkup, D., and Church, G. M. (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. USA 99, 15112–15117.

    Article  PubMed  Google Scholar 

  25. Shlomi, T., Berkman, O., and Ruppin, E. (2005) Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc. Natl. Acad. Sci. USA 102, 7695–7700.

    Article  PubMed  CAS  Google Scholar 

  26. Snitkin, E. S., and Segre, D. (2008) Optimality criteria for the prediction of metabolic fluxes in yeast mutants. Genome Inform. 20, 123–134.

    Article  PubMed  CAS  Google Scholar 

  27. Deutschbauer, A. M., Jaramillo, D. F., Proctor, M., et al. (2005) Mechanisms of haploinsufficiency revealed by genome-wide profiling in yeast. Genetics 169, 1915–1925

    Article  PubMed  CAS  Google Scholar 

  28. Warringer, J., Ericson, E., Fernandez, L., Nerman, O., and Blomberg, A. (2003) High-resolution yeast phenomics resolves different physiological features in the saline response. Proc. Natl. Acad. Sci. USA 100, 15724–15729.

    Article  PubMed  CAS  Google Scholar 

  29. Schuster, S., Pfeiffer, T., and Fell, D. A. (2008) Is maximization of molar yield in metabolic networks favoured by evolution? J. Theor. Biol. 252, 497–504.

    Article  PubMed  CAS  Google Scholar 

  30. Gancedo, J. M. (1998) Yeast carbon catabolite repression. Microbiol. Mol. Biol. Rev. 62, 334–361.

    PubMed  CAS  Google Scholar 

  31. Sonnleitner, B., and Kappeli, O. (1986) Growth of Saccharomyces cerevisiae is controlled by its limited respiratory capacity: formulation and verification of a hypothesis. Biotechnol. Bioeng. 28, 927–937.

    Article  PubMed  CAS  Google Scholar 

  32. Schuurmans, J. M., Boorsma, A., Lascaris, R., Hellingwerf, K. J., and Teixeira de Mattos, M. J. (2008) Physiological and transcriptional characterization of Saccharomyces cerevisiae strains with modified expression of catabolic regulators. FEMS Yeast Res. 8, 26–34.

    Article  PubMed  CAS  Google Scholar 

  33. Usaite, R., Jewett, M. C., Oliveira, A. P., Yates, J. R., 3rd, Olsson, L., and Nielsen, J. (2009) Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator. Mol. Syst. Biol. 5, 319.

    Article  PubMed  Google Scholar 

  34. Boone, C., Bussey, H., and Andrews, B. J. (2007) Exploring genetic interactions and networks with yeast. Nat. Rev. Genet. 8, 437–449.

    Article  PubMed  CAS  Google Scholar 

  35. Wolf, J. B., Brodie, E. D., and Wade, M. J. (2000) Epistasis and the Evolutionary Process. New York, NY: Oxford University Press.

    Google Scholar 

  36. Segrè, D., Deluna, A., Church, G. M., and Kishony, R. (2005) Modular epistasis in yeast metabolism. Nat. Genet. 37, 77–83.

    PubMed  Google Scholar 

  37. Deutscher, D., Meilijson, I., Kupiec, M., and Ruppin, E. (2006) Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nat. Genet. 38, 993–998.

    Article  PubMed  CAS  Google Scholar 

  38. Tong, A. H., Lesage, G., Bader, G. D., et al. (2004) Global mapping of the yeast genetic interaction network. Science 303, 808–813.

    Article  PubMed  CAS  Google Scholar 

  39. Schuldiner, M., Collins, S. R., Thompson, N. J., et al. (2005) Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123, 507–519.

    Article  PubMed  CAS  Google Scholar 

  40. Giaever, G., Chu, A. M., Ni, L., et al. (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391.

    Article  PubMed  CAS  Google Scholar 

  41. Hurst, L. D., and Pál, C. (2007) Genomic redundancy and dispensability. In: Pagel, M., and Pomiankowski, A. (eds.), Evolutionary Genomics and Proteomics (pp. 141–160). Sunderland, MA: Sinauer Associates Inc.

    Google Scholar 

  42. Wagner, A. (2000) Robustness against mutations in genetic networks of yeast. Nat. Genet. 24, 355–361.

    Article  PubMed  CAS  Google Scholar 

  43. Blank, L. M., Kuepfer, L., and Sauer, U. (2005) Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast. Genome Biol. 6, R49.

    Article  PubMed  Google Scholar 

  44. Nishikawa, T., Gulbahce, N., and Motter, A. E. (2008) Spontaneous reaction silencing in metabolic optimization. PLoS Comput. Biol. 4, e1000236.

    Article  PubMed  Google Scholar 

  45. Hillenmeyer, M. E., Fung, E., Wildenhain, J., et al. (2008) The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science 320, 362–365.

    Article  PubMed  CAS  Google Scholar 

  46. Ihmels, J., Collins, S. R., Schuldiner, M., Krogan, N. J., and Weissman, J. S. (2007) Backup without redundancy: genetic interactions reveal the cost of duplicate gene loss. Mol. Syst. Biol. 3, 86.

    Article  PubMed  Google Scholar 

  47. Musso, G., Costanzo, M., Huangfu, M., et al. (2008) The extensive and condition-dependent nature of epistasis among whole-genome duplicates in yeast. Genome Res. 18, 1092–1099.

    Article  PubMed  CAS  Google Scholar 

  48. Papp, B., Teusink, B., and Notebaart, R. A. (2009) A critical view of metabolic network adaptations. HFSP J. 3, 24–35.

    Article  PubMed  Google Scholar 

  49. Travisano, M., Mongold, J. A., Bennett, A. F., and Lenski, R. E. (1995) Experimental tests of the roles of adaptation, chance, and history in evolution. Science 267, 87–90.

    Article  PubMed  CAS  Google Scholar 

  50. Pál, C., Papp, B., and Lercher, M. J. (2005) Adaptive evolution of bacterial metabolic networks by horizontal gene transfer. Nat. Genet. 37, 1372–1375.

    Article  PubMed  Google Scholar 

  51. Parker, G. A., and Smith, J. M. (1990) Optimality theory in evolutionary biology. Nature 348, 27–33.

    Article  Google Scholar 

  52. Schuetz, R., Kuepfer, L., and Sauer, U. (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol. Syst. Biol. 3, 119.

    Article  PubMed  Google Scholar 

  53. Pfeiffer, T., and Schuster, S. (2005) Game-theoretical approaches to studying the evolution of biochemical systems. Trends Biochem. Sci. 30, 20–25.

    Article  PubMed  CAS  Google Scholar 

  54. MacLean, R. C. (2008) The tragedy of the commons in microbial populations: insights from theoretical, comparative and experimental studies. Heredity 100, 471–477.

    Article  PubMed  CAS  Google Scholar 

  55. Ibarra, R. U., Edwards, J. S., and Palsson, B. O. (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186–189.

    Article  PubMed  CAS  Google Scholar 

  56. Herring, C. D., Raghunathan, A., Honisch, C., et al. (2006) Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale. Nat. Genet. 38, 1406–1412.

    Article  PubMed  CAS  Google Scholar 

  57. Teusink, B., Wiersma, A., Jacobs, L., Notebaart, R. A., and Smid, E. J. (2009) Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation. PLoS Comput. Biol. 5, e1000410.

    Article  PubMed  Google Scholar 

  58. Fong, S. S., and Palsson, B. O. (2004) Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat. Genet. 36, 1056–1058.

    Article  PubMed  CAS  Google Scholar 

  59. Beard, D. A., Liang, S. D., and Qian, H. (2002) Energy balance for analysis of complex metabolic networks. Biophys. J. 83, 79–86.

    Article  PubMed  CAS  Google Scholar 

  60. Covert, M. W., Schilling, C. H., and Palsson, B. (2001) Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 213, 73–88.

    Article  PubMed  CAS  Google Scholar 

  61. Sauer, U. (2006) Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2, 62.

    Article  PubMed  Google Scholar 

  62. Ishii, N., Nakahigashi, K., Baba, T., et al. (2007) Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316, 593–597.

    Article  PubMed  CAS  Google Scholar 

  63. Akesson, M., Forster, J., and Nielsen, J. (2004) Integration of gene expression data into genome-scale metabolic models. Metab. Eng. 6, 285–293.

    Article  PubMed  CAS  Google Scholar 

  64. Hoppe, A., Hoffmann, S., and Holzhutter, 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, 23.

    Article  PubMed  Google Scholar 

  65. Allen, J., Davey, H. M., Broadhurst, D., et al. (2003) High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696.

    Article  PubMed  CAS  Google Scholar 

  66. Hughes, T. R., Marton, M. J., Jones, A. R., et al. (2000) Functional discovery via a compendium of expression profiles. Cell 102, 109–126.

    Article  PubMed  CAS  Google Scholar 

  67. Smallbone, K., Simeonidis, E., Broomhead, D. S., and Kell, D. B. (2007) Something from nothing: bridging the gap between constraint-based and kinetic modelling. FEBS J 274, 5576–5585.

    Article  PubMed  CAS  Google Scholar 

  68. Covert, M. W., Xiao, N., Chen, T. J., and Karr, J. R. (2008) Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 24, 2044–2050.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We thank Csaba Pál for suggestions on the manuscript and Kiran Patil and Juan I. Castrillo for comments on the issue of quantitative fitness predictions with FBA. B.P. is supported by The International Human Frontier Science Program Organization, the Hungarian Scientific Research Fund (OTKA), the “Lendület Program,” and the Bolyai Fellowship of the Hungarian Academy of Sciences. R.N. is supported by The Netherlands Genomics Initiative (NGI – Horizon grant) and The Netherlands Organisation for Scientific Research (NWO – VENI Grant).

Author information

Authors and Affiliations

  1. Institute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Szeged, Hungary

    Balázs Papp

  2. Department of Genetics, Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK

    Balázs Papp

  3. Institute of Biochemistry, Biological Research Center, Szeged, Hungary

    Balázs Szappanos

  4. Centre for Molecular and Biomolecular Informatics (NCMLS), Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

    Richard A. Notebaart

  5. Centre for Systems Biology and Bioenergetics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

    Richard A. Notebaart

Authors
  1. Balázs Papp
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Balázs Szappanos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard A. Notebaart
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Balázs Papp .

Editor information

Editors and Affiliations

  1. , Department of Biochemistry, University of Cambridge, Tennis Court Road 80, Cambridge, CB2 1GA, United Kingdom

    Juan I. Castrillo

  2. , Department of Biochemistry, University of Cambridge, Tennis Court Road 80, Cambridge, CB2 1GA, United Kingdom

    Stephen G. Oliver

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Humana Press

About this protocol

Cite this protocol

Papp, B., Szappanos, B., Notebaart, R.A. (2011). Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology. In: Castrillo, J., Oliver, S. (eds) Yeast Systems Biology. Methods in Molecular Biology, vol 759. Humana Press. https://doi.org/10.1007/978-1-61779-173-4_27

Download citation

  • DOI: https://doi.org/10.1007/978-1-61779-173-4_27

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-172-7

  • Online ISBN: 978-1-61779-173-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation