Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods

Nature Reviews Microbiology volume 10, pages 291–305 (2012)Cite this article

Subjects

Key Points

  • Our understanding of genotype–phenotype relationships has classically been qualitative, but recent advances are enabling us to overcome conceptual and technological barriers, leading to a quantitative understanding of these relationships. Within the framework of constraint-based modelling, the generation of quantitative relationships is facilitated by the realization that cell phenotypes are limited by physical and genetic constraints.

  • Physical laws, such as mass conservation and thermodynamics, constrain the possible metabolic and biosynthetic transformations that can occur in nature, and genetics specify which sets of biochemical reactions have been selected through evolution. Genome sequencing and annotation have allowed the comprehensive reconstruction of microbial metabolic networks, and constraint-based reconstruction and analysis (COBRA) modelling has emerged as a set of valuable tools that allows detailed analysis of the biochemical mechanisms underlying the metabolic genotype–phenotype relationship.

  • Network-based pathway analysis tools, such as elementary flux modes and extreme pathways analysis, delineate pathways that can perform a given metabolic function in an organism of interest. Although these methods have been difficult to use in larger metabolic networks, simplifications are now beginning to allow their use on genome-scale models.

  • As not all pathways are used in a cell at a given time, optimization algorithms are routinely used to identify pathway use that best reflects the in vivo metabolic state. Flux balance analysis, which uses linear programing to optimize a mathematical description of the cellular objective, has been widely used to understand microbial physiology and the effects of environmental and genetic perturbations.

  • The ability of COBRA methods to model genetic perturbations has allowed such methods to help predict antimicrobial targets and to aid in the design of strains for chemical production.

  • Reconstructed metabolic networks are often incomplete and can have a small fraction of incorrect reactions therein. However, the integration of phenotypic screens with model simulations can provide a systematic approach to refine the models and discover new metabolic functions in an organism.

  • COBRA methods are extending beyond metabolism, and approaches are beginning to incorporate transcription regulation, either implicitly, by constraining models with multiple 'omic data types, or explicitly, with detailed descriptions of regulatory mechanisms.

  • The diverse range of more than 100 COBRA methods is being deployed to address many questions in microbiology. For example, several recent studies have begun to explore the roles of metabolism in community interactions, including symbiosis, competition, parasitism and evolution.

Abstract

Reconstructed microbial metabolic networks facilitate a mechanistic description of the genotype–phenotype relationship through the deployment of constraint-based reconstruction and analysis (COBRA) methods. As reconstructed networks leverage genomic data for insight and phenotype prediction, the development of COBRA methods has accelerated following the advent of whole-genome sequencing. Here, we describe a phylogeny of COBRA methods that has rapidly evolved from the few early methods, such as flux balance analysis and elementary flux mode analysis, into a repertoire of more than 100 methods. These methods have enabled genome-scale analysis of microbial metabolism for numerous basic and applied uses, including antibiotic discovery, metabolic engineering and modelling of microbial community behaviour.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Fundamentals of the genome-scale metabolic genotype–phenotype relationship.
Figure 2: The 'phylogeny' of constraint-based modelling methods.
Figure 3: Flux balance analysis.
Figure 4: Principles of model-guided strain design.
Figure 5: Refining thermodynamic constraints.
Figure 6: Incorporating regulation.
Figure 7: Integrating constraint-based reconstruction and analysis methods to study community interactions.

Similar content being viewed by others

References

  1. Feist, A. M., Herrgård, M. J., Thiele, I., Reed, J. L. & Palsson, B. Ø. Reconstruction of biochemical networks in microorganisms. Nature Rev. Microbiol. 7, 129–143 (2009). This review provides the detailed concepts of metabolic network reconstruction.

    CAS  Google Scholar 

  2. Thiele, I. & Palsson, B. Ø. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protoc. 5, 93–121 (2010).

    CAS  Google Scholar 

  3. Henry, C. S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature Biotech. 28, 977–982 (2010).

    Article  CAS  Google Scholar 

  4. Feist, A. M. & Palsson, B. Ø. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nature Biotech. 26, 659–667 (2008).

    Article  CAS  Google Scholar 

  5. Oberhardt, M. A., Palsson, B. Ø. & Papin, J. A. Applications of genome-scale metabolic reconstructions. Mol. Syst. Biol. 5, 320 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Papp, B., Notebaart, R. A. & Pal, C. Systems-biology approaches for predicting genomic evolution. Nature Rev. Genet. 12, 591–602 (2011). A thorough review of how COBRA methods aid in the study of evolution.

    Article  CAS  PubMed  Google Scholar 

  7. Mahadevan, R., Palsson, B. Ø. & Lovley, D. R. In situ to in silico and back: elucidating the physiology and ecology of Geobacter spp. using genome-scale modelling. Nature Rev. Microbiol. 9, 39–50 (2011).

    Article  CAS  Google Scholar 

  8. Palsson, B. Ø. Systems Biology: Properties of Reconstructed Networks (Cambridge Univ. Press, 2006).

    Book  Google Scholar 

  9. Feist, A. M. & Palsson, B. O. The biomass objective function. Curr. Opin. Microbiol. 13, 344–349 (2010). A description of how the biomass objective function is formulated.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Fell, D. A. & Small, J. R. Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochem. J. 238, 781–786 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Watson, M. R. Metabolic maps for the Apple II. Biochem. Soc. Trans. 12, 1093–1094 (1984).

    Article  CAS  Google Scholar 

  12. Edwards, J. S. & Palsson, B. O. Systems properties of the Haemophilus influenzae Rd metabolic genotype. J. Biol. Chem. 274, 17410–17416 (1999).

    Article  CAS  PubMed  Google Scholar 

  13. Edwards, J. S. & Palsson, B. O. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl Acad. Sci. USA 97, 5528–5533 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Reed, J. L., Famili, I., Thiele, I. & Palsson, B. O. Towards multidimensional genome annotation. Nature Rev. Genet. 7, 130–141 (2006).

    Article  CAS  PubMed  Google Scholar 

  15. Kim, T. Y., Kim, H. U. & Lee, S. Y. Data integration and analysis of biological networks. Curr. Opin. Biotechnol. 21, 78–84 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  17. Papin, J. A. et al. Comparison of network-based pathway analysis methods. Trends Biotechnol. 22, 400–405 (2004). An assessment of the differences between EFM and ExPa analysis.

    Article  CAS  PubMed  Google Scholar 

  18. Trinh, C. T., Wlaschin, A. & Srienc, F. Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Appl. Microbiol. Biotechnol. 81, 813–826 (2009).

    Article  CAS  PubMed  Google Scholar 

  19. Llaneras, F. & Pico, J. Which metabolic pathways generate and characterize the flux space? A comparison among elementary modes, extreme pathways and minimal generators. J. Biomed. Biotechnol. 2010, 753904 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S. & Gilles, E. D. Metabolic network structure determines key aspects of functionality and regulation. Nature 420, 190–193 (2002).

    Article  CAS  PubMed  Google Scholar 

  21. Trinh, C. T., Unrean, P. & Srienc, F. Minimal Escherichia coli cell for the most efficient production of ethanol from hexoses and pentoses. Appl. Environ. Microbiol. 74, 3634–3643 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Imielinski, M. & Belta, C. Exploiting the pathway structure of metabolism to reveal high-order epistasis. BMC Syst. Biol. 2, 40 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wessely, F. et al. Optimal regulatory strategies for metabolic pathways in Escherichia coli depending on protein costs. Mol. Syst. Biol. 7, 515 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Schilling, C. H. & Palsson, B. Ø. Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. J. Theor. Biol. 203, 249–283 (2000).

    Article  CAS  PubMed  Google Scholar 

  25. Yeung, M., Thiele, I. & Palsson, B. Ø. Estimation of the number of extreme pathways for metabolic networks. BMC Bioinformatics 8, 363 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Klamt, S. & Stelling, J. Combinatorial complexity of pathway analysis in metabolic networks. Mol. Biol. Rep. 29, 233–236 (2002).

    Article  CAS  PubMed  Google Scholar 

  27. Kaleta, C., de Figueiredo, L. F. & Schuster, S. Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns. Genome Res. 19, 1872–1883 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Rezola, A. et al. Exploring metabolic pathways in genome-scale networks via generating flux modes. Bioinformatics 27, 534–540 (2011).

    Article  CAS  PubMed  Google Scholar 

  29. Ip, K., Colijn, C. & Lun, D. S. Analysis of complex metabolic behavior through pathway decomposition. BMC Syst. Biol. 5, 91 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Chan, S. H. & Ji, P. Decomposing flux distributions into elementary flux modes in genome-scale metabolic networks. Bioinformatics 27, 2256–2262 (2011).

    Article  CAS  PubMed  Google Scholar 

  31. Braunstein, A., Mulet, R. & Pagnani, A. Estimating the size of the solution space of metabolic networks. BMC Bioinformatics 9, 240 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Schellenberger, J. & Palsson, B. Ø. Use of randomized sampling for analysis of metabolic networks. J. Biol. Chem. 284, 5457–5461 (2009).

    Article  CAS  PubMed  Google Scholar 

  33. Almaas, E., Kovacs, B., Vicsek, T., Oltvai, Z. N. & Barabasi, A. L. Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature 427, 839–843 (2004).

    Article  CAS  PubMed  Google Scholar 

  34. Bordel, S., Agren, R. & Nielsen, J. Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput. Biol. 6, e1000859 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mo, M. L., Palsson, B. Ø. & Herrgård, M. J. Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst. Biol. 3, 37 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Barrett, C. L., Herrgard, M. J. & Palsson, B. Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation. BMC Syst. Biol. 3, 30 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Thiele, I., Price, N. D., Vo, T. D. & Palsson, B. Ø. Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet. J. Biol. Chem. 280, 11683–11695 (2005).

    Article  CAS  PubMed  Google Scholar 

  38. Price, N. D., Schellenberger, J. & Palsson, B. O. Uniformsampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys. J. 87, 2172–2186 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lewis, N. E. et al. Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nature Biotech. 28, 1279–1285 (2010).

    Article  CAS  Google Scholar 

  40. Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nature Biotech. 28, 245–248 (2010). A primer to the FBA method.

    Article  CAS  Google Scholar 

  41. Varma, A. & Palsson, B. O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 60, 3724–3731 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Edwards, J. S., Ibarra, R. U. & Palsson, B. O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotech. 19, 125–130 (2001).

    Article  CAS  Google Scholar 

  43. Lewis, N. E. et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 6, 390 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wang, Z. & Zhang, J. Impact of gene expression noise on organismal fitness and the efficacy of natural selection. Proc. Natl Acad. Sci. USA 108, E67–E76 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Goffin, P. et al. Understanding the physiology of Lactobacillus plantarum at zero growth. Mol. Syst. Biol. 6, 413 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lee, S., Palakornkule, C., Domach, M. M. & Grossmann, I. E. Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Comput. Chem. Eng. 24, 711–716 (2000).

    Article  CAS  Google Scholar 

  48. Gudmundsson, S. & Thiele, I. Computationally efficient flux variability analysis. BMC Bioinformatics 11, 489 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Burgard, A. P., Vaidyaraman, S. & Maranas, C. D. Minimal reaction sets for Escherichia coli metabolism under different growth requirements and uptake environments. Biotechnol. Prog. 17, 791–797 (2001).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Benyamini, T., Folger, O., Ruppin, E. & Shlomi, T. Flux balance analysis accounting for metabolite dilution. Genome Biol. 11, R43 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Colijn, C. et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol. 5, e1000489 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. van Berlo, R. J. P. et al. Predicting metabolic fluxes using gene expression differences as constraints. IEEE/ACM Trans. Comput. Biol. Bioinform. 8, 206–216 (2011).

    Article  PubMed  Google Scholar 

  54. Holzhutter, H. G. The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur. J. Biochem. 271, 2905–2922 (2004).

    Article  CAS  PubMed  Google Scholar 

  55. Ponce de Leon, M., Cancela, H. & Acerenza, L. A strategy to calculate the patterns of nutrient consumption by microorganisms applying a two-level optimisation principle to reconstructed metabolic networks. J. Biol. Phys. 34, 73–90 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Murabito, E., Simeonidis, E., Smallbone, K. & Swinton, J. Capturing the essence of a metabolic network: a flux balance analysis approach. J. Theor. Biol. 260, 445–452 (2009).

    Article  CAS  PubMed  Google Scholar 

  57. Beg, Q. K. et al. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc. Natl Acad. Sci. USA 104, 12663–12668 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Vazquez, A., Markert, E. K. & Oltvai, Z. N. Serine biosynthesis with one carbon catabolism and the glycine cleavage system represents a novel pathway for ATP generation. PLoS ONE 6, e25881 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Zhuang, K., Vemuri, G. N. & Mahadevan, R. Economics of membrane occupancy and respiro-fermentation. Mol. Syst. Biol. 7, 500 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kim, P. J. et al. Metabolite essentiality elucidates robustness of Escherichia coli metabolism. Proc. Natl Acad. Sci. USA 104, 13638–13642 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Chang, R. L., Xie, L., Xie, L., Bourne, P. E. & Palsson, B. Ø. Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput. Biol. 6, e1000938 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Shen, Y. et al. Blueprint for antimicrobial hit discovery targeting metabolic networks. Proc. Natl Acad. Sci. USA 107, 1082–1087 (2010). A study in which metabolic networks are used to search for antimicrobials.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kim, H. U. et al. Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery. Mol. Syst. Biol. 7, 460 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Fong, S. S. et al. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol. Bioeng. 91, 643–648 (2005).

    Article  CAS  PubMed  Google Scholar 

  68. Burgard, A. P., Pharkya, P. & Maranas, C. D. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647–657 (2003).

    Article  CAS  PubMed  Google Scholar 

  69. Feist, A. M. et al. Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metab. Eng. 12, 173–186 (2010).

    Article  CAS  PubMed  Google Scholar 

  70. Tepper, N. & Shlomi, T. Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics 26, 536–543 (2010).

    Article  CAS  PubMed  Google Scholar 

  71. Patil, K. R., Rocha, I., Forster, J. & Nielsen, J. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6, 308 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Lun, D. S. et al. Large-scale identification of genetic design strategies using local search. Mol. Syst. Biol. 5, 296 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Yousofshahi, M., Lee, K. & Hassoun, S. Probabilistic pathway construction. Metab. Eng. 13, 435–444 (2011).

    Article  CAS  PubMed  Google Scholar 

  74. Rodrigo, G., Carrera, J., Prather, K. J. & Jaramillo, A. DESHARKY: automatic design of metabolic pathways for optimal cell growth. Bioinformatics 24, 2554–2556 (2008).

    Article  CAS  PubMed  Google Scholar 

  75. Bar-Even, A., Noor, E., Lewis, N. E. & Milo, R. Design and analysis of synthetic carbon fixation pathways. Proc. Natl Acad. Sci. USA 107, 8889–8894 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Delebecque, C. J., Lindner, A. B., Silver, P. A. & Aldaye, F. A. Organization of intracellular reactions with rationally designed RNA assemblies. Science 333, 470–474 (2011).

    Article  CAS  PubMed  Google Scholar 

  77. Yim, H. et al. Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nature Chem. Biol. 7, 445–452 (2011).A detailed study in which several computational and experimental technologies are used to engineer a microorganism to synthesize 1,4-butanediol using.

    Article  CAS  Google Scholar 

  78. Pharkya, P., Burgard, A. P. & Maranas, C. D. OptStrain: a computational framework for redesign of microbial production systems. Genome Res. 14, 2367–2376 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Szappanos, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nature Genet. 43, 656–662 (2011).

    Article  CAS  PubMed  Google Scholar 

  80. Hsiao, T. L., Revelles, O., Chen, L., Sauer, U. & Vitkup, D. Automatic policing of biochemical annotations using genomic correlations. Nature Chem. Biol. 6, 34–40 (2010).

    Article  CAS  Google Scholar 

  81. Bordbar, A., Lewis, N. E., Schellenberger, J., Palsson, B. Ø. & Jamshidi, N. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol. Syst. Biol. 6, 422 (2010).

    PubMed  PubMed Central  Google Scholar 

  82. Schuster, S., Pfeiffer, T. & Fell, D. A. Is maximization of molar yield in metabolic networks favoured by evolution? J. Theor. Biol. 252, 497–504 (2008).A critical assessment of the assumptions in FBA.

    Article  CAS  PubMed  Google Scholar 

  83. Milne, C. B. et al. Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052. BMC Syst. Biol. 5, 130 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Reed, J. L. et al. Systems approach to refining genome annotation. Proc. Natl Acad. Sci. USA 103, 17480–17484 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Orth, J. D. & Palsson, B. Ø. Systematizing the generation of missing metabolic knowledge. Biotechnol. Bioeng. 107, 403–412 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Satish Kumar, V., Dasika, M. S. & Maranas, C. D. Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8, 212 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Kumar, V. S. & Maranas, C. D. GrowMatch: an automated method for reconciling in silico/in vivo growth predictions. PLoS Comput. Biol. 5, e1000308 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Suthers, P. F., Zomorrodi, A. & Maranas, C. D. Genome-scale gene/reaction essentiality and synthetic lethality analysis. Mol. Syst. Biol. 5, 301 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Mintz-Oron, S., Aharoni, A., Ruppin, E. & Shlomi, T. Network-based prediction of metabolic enzymes' subcellular localization. Bioinformatics 25, i247–i252 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Burgard, A. P. & Maranas, C. D. Optimization-based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol. Bioeng. 82, 670–677 (2003).

    Article  CAS  PubMed  Google Scholar 

  91. Knorr, A. L., Jain, R. & Srivastava, R. Bayesian-based selection of metabolic objective functions. Bioinformatics 23, 351–357 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Gianchandani, E. P., Oberhardt, M. A., Burgard, A. P., Maranas, C. D. & Papin, J. A. Predicting biological system objectives de novo from internal state measurements. BMC Bioinformatics 9, 43 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Zomorrodi, A. R. & Maranas, C. D. Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data. BMC Syst. Biol. 4, 178 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Schellenberger, J., Lewis, N. E. & Palsson, B. Ø. Elimination of thermodynamically infeasible loops in steady-state metabolic models. Biophys. J. 100, 544–553 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Price, N. D., Thiele, I. & Palsson, B. Ø. Candidate states of Helicobacter pylori's genome-scale metabolic network upon application of “loop law” thermodynamic constraints. Biophys. J. 90, 3919–3928 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Henry, C. S., Broadbelt, L. J. & Hatzimanikatis, V. Thermodynamics-based metabolic flux analysis. Biophys. J. 92, 1792–1805 (2007).

    Article  CAS  PubMed  Google Scholar 

  99. Fleming, R. M., Thiele, I., Provan, G. & Nasheuer, H. P. Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism. J. Theor. Biol. 264, 683–692 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Kummel, A., Panke, S. & Heinemann, M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2, 2006.0034 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Jankowski, M. D., Henry, C. S., Broadbelt, L. J. & Hatzimanikatis, V. Group contribution method for thermodynamic analysis of complex metabolic networks. Biophys. J. 95, 1487–1499 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Henry, C. S., Jankowski, M. D., Broadbelt, L. J. & Hatzimanikatis, V. Genome-scale thermodynamic analysis of Escherichia coli metabolism. Biophys. J. 90, 1453–1461 (2006).

    Article  CAS  PubMed  Google Scholar 

  103. Hoppe, A., Hoffmann, S. & Holzhutter, H. G. Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks. BMC Syst. Biol. 1, 23 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Fleming, R. M., Thiele, I. & Nasheuer, H. P. Quantitative assignment of reaction directionality in constraint-based models of metabolism: application to Escherichia coli. Biophys. Chem. 145, 47–56 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Becker, S. A. & Palsson, B. O. Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol. 4, e1000082 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Shlomi, T., Cabili, M. N., Herrgård, M. J., Palsson, B.Ø. & Ruppin, E. Network-based prediction of human tissue-specific metabolism. Nature Biotech. 26, 1003–1010 (2008).

    Article  CAS  Google Scholar 

  107. Jerby, L., Shlomi, T. & Ruppin, E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol. Syst. Biol. 6, 401 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Jensen, P. A. & Papin, J. A. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 27, 541–547 (2011).

    Article  CAS  PubMed  Google Scholar 

  109. Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J. & Palsson, B. O. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429, 92–96 (2004).

    Article  CAS  PubMed  Google Scholar 

  110. Shlomi, T., Eisenberg, Y., Sharan, R. & Ruppin, E. A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol. Syst. Biol. 3, 101 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Lee, J. M., Gianchandani, E. P., Eddy, J. A. & Papin, J. A. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput. Biol. 4, e1000086 (2008).

    Article  PubMed  Google Scholar 

  113. Folger, O. et al. Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol. 7, 501 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Frezza, C. et al. Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477, 225–228 (2011).

    Article  CAS  PubMed  Google Scholar 

  115. Herrgård, M. J., Lee, B. S., Portnoy, V. & Palsson, B. Ø. Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res. 16, 627–635 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Chandrasekaran, S. & Price, N. D. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 107, 17845–17850 (2010).An approach for integrating regulatory networks with metabolic modelling.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Pal, C. et al. Chance and necessity in the evolution of minimal metabolic networks. Nature 440, 667–670 (2006).

    Article  CAS  PubMed  Google Scholar 

  118. Stolyar, S. et al. Metabolic modeling of a mutualistic microbial community. Mol. Syst. Biol. 3, 92 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Taffs, R. et al. In silico approaches to study mass and energy flows in microbial consortia: a syntrophic case study. BMC Syst. Biol. 3, 114 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Klitgord, N. & Segre, D. Environments that induce synthetic microbial ecosystems. PLoS Comput. Biol. 6, e1001002 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Zhuang, K. et al. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J. 5, 305–316 (2011).

    Article  PubMed  Google Scholar 

  123. Huthmacher, C., Hoppe, A., Bulik, S. & Holzhutter, H. G. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis. BMC Syst. Biol. 4, 120 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Yizhak, K., Tuller, T., Papp, B. & Ruppin, E. Metabolic modeling of endosymbiont genome reduction on a temporal scale. Mol. Syst. Biol. 7, 479 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Bonde, B. K., Beste, D. J., Laing, E., Kierzek, A. M. & McFadden, J. Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis. PLoS Comput. Biol. 7, e1002060 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Nam, H., Conrad, T. M. & Lewis, N. E. The role of cellular objectives and selective pressures in metabolic pathway evolution. Curr. Opin. Biotechnol. 22, 595–600 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Srinivasan, K. & Mahadevan, R. Characterization of proton production and consumption associated with microbial metabolism. BMC Biotechnol. 10, 2 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Thiele, I., Jamshidi, N., Fleming, R. M. & Palsson, B. Ø. Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Comput. Biol. 5, e1000312 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Thiele, I., Fleming, R. M., Bordbar, A., Schellenberger, J. & Palsson, B. Ø. Functional characterization of alternate optimal solutions of Escherichia coli's transcriptional and translational machinery. Biophys. J. 98, 2072–2081 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Sigurdsson, M. I., Jamshidi, N., Steingrimsson, E., Thiele, I. & Palsson, B. Ø. A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1. BMC Syst. Biol. 4, 140 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Sweetlove, L. J. & Ratcliffe, R. G. Flux-balance modelling of plant metabolism. Frontiers Plant Sci. 2, 38 (2011).

    Article  CAS  Google Scholar 

  132. Gille, C. et al. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol. Syst. Biol. 6, 411 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Kim, J. & Reed, J. L. OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst. Biol. 4, 53 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Bordbar, A. et al. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC Syst. Biol. 5, 180 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Zhang, Y. et al. Three-dimensional structural view of the central metabolic network of Thermotoga maritima. Science 325, 1544–1549 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Barua, D., Kim, J. & Reed, J. L. An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. PLoS Comput. Biol. 6, e1000970 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Lee, K. H., Park, J. H., Kim, T. Y., Kim, H. U. & Lee, S. Y. Systems metabolic engineering of Escherichia coli for L-threonine production. Mol. Syst. Biol. 3, 149 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Alper, H., Jin, Y. S., Moxley, J. F. & Stephanopoulos, G. Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. 7, 155–164 (2005).

    Article  CAS  PubMed  Google Scholar 

  139. Kennedy, C. J., Boyle, P. M., Waks, Z. & Silver, P. A. Systems-level engineering of nonfermentative metabolism in yeast. Genetics 183, 385–397 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Xu, P., Ranganathan, S., Fowler, Z. L., Maranas, C. D. & Koffas, M. A. Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA. Metab. Eng. 13, 578–587 (2011).

    Article  CAS  PubMed  Google Scholar 

  141. Chemler, J. A., Fowler, Z. L., McHugh, K. P. & Koffas, M. A. Improving NADPH availability for natural product biosynthesis in Escherichia coli by metabolic engineering. Metab. Eng. 12, 96–104 (2010).

    Article  CAS  PubMed  Google Scholar 

  142. Tepper, N. & Shlomi, T. Computational design of auxotrophy-dependent microbial biosensors for combinatorial metabolic engineering experiments. PLoS ONE 6, e16274 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank M. Abrams, J. Monk, D. Taylor, E. O'Brien, A. Feist, J. Lerman, D. Zielinski, R. Chang, K. Zengler, A. Bordbar, R. Fleming, N. Price and E. Ruppin for discussions and comments on this Review, and numerous members of the COBRA community for help with compiling methods. This work was funded by the US National Institutes of Health (grant R01GM057089) and the Office of Science (Biological and Environmental Research) for the US Department of Energy (grant DE-SC0004485).

Author information

Authors and Affiliations

  1. Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093–0412, California, USA

    Nathan E. Lewis & Bernhard O. Palsson

  2. Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093–0412, California, USA

    Harish Nagarajan

Authors
  1. Nathan E. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harish Nagarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bernhard O. Palsson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bernhard O. Palsson.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information s1 (table)

Description and classification of constaint-based methods. (XLS 151 kb)

Supplementary information s2 (table)

Software packages that have implemented multiple constraint-based methods. (XLS 34 kb)

Related links

Related links

FURTHER INFORMATION

Bernhard O. Palsson's homepage

COBRA software

Glossary

Metabolic reconstruction

A carefully curated and biochemically validated knowledge base in which all known chemical reactions for an organism are detailed and catalogued.

Solution space

The feasible region satisfying a set of constraints. In constraint-based reconstruction and analysis (COBRA) models, this represents the feasible flux values for all of the reactions in the model.

Flux distributions

A set of steady-state fluxes for all of the reactions in a metabolic network.

Biomass function

A pseudo-reaction that is formed to aid in predicting the growth of a cell in constraint-based reconstruction and analysis (COBRA) models. It describes the rate at which and accurate proportions in which all of the biomass precursors are made.

Linear programing

A mathematical optimization technique that determines a way to maximize a particular linear objective under a given set of conditions (that is, when subjected to linear equalities and inequalities as constraints). Typically used in flux balance analysis, in which the objective is often the biomass function (growth) and the constraints represent the growth conditions.

Genome-scale model

(GEM). A condition-specific, mathematically described, computable derivative of a metabolic reconstruction, containing comprehensive knowledge of metabolism.

Mixed-integer linear programing

(MILP). Similar to linear programing, but some of the constraints are integer values. Used for applications such as enumerating alternative optimal solutions, strain design, eliminating loops, and so on.

Epistasis

The interaction between two genes such that the phenotypic effect of one gene is masked by that of the other. Usually identified by the phenotype of the double mutant relative to the phenotypes of the two single mutants.

Shadow prices

A mathematical term that refers to the dual aspects of the linear programing problem. It represents the rate at which the objective value (for example, growth rate) changes as the supply of a particular resource (for example, a metabolite) increases.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lewis, N., Nagarajan, H. & Palsson, B. Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10, 291–305 (2012). https://doi.org/10.1038/nrmicro2737

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrmicro2737

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research