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Applications of metabolic modeling to drive bioprocess development for the production of value-added chemicals

Biotechnology and Bioprocess Engineering volume 10, pages 408–417 (2005)Cite this article

Abstract

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput ‘omics’ data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.

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References

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

    Article  CAS  Google Scholar 

  2. Edwards, J. S. and B. O. Palsson (2000) TheEscherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities.Proc. Natl. Acad. Sci. USA 97: 5528–5533.

    Article  CAS  Google Scholar 

  3. Reed, J. L., T. D. Vo, C. H. Schilling, and B. Palsson (2003)Escherichia coli iJR904: An expanded genomescale model ofE. coli K-12.Genome Biol. 4: R54.1-R54.12.

    Article  Google Scholar 

  4. Famili, I., J. Forster, J. Nielsen, and B. O. Palsson (2003)Saccharomyces: cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network.Proc. Natl. Acad. Sci. USA 100: 13134–13139.

    Article  CAS  Google Scholar 

  5. Dauner, M. and U. Sauer (2001) Stoichiometric growth model for riboflavin-producingBacillus subtilis.Biotechnol. Bioeng. 76: 132–143.

    Article  CAS  Google Scholar 

  6. Hong, S. H., J. S. Kim, S. Y. Lee, Y. H. In, S. S. Choi, J. K. Rih, C. H. Kim, H. Jeong, C. G. Hur, and J. J. Kim (2004) The genome sequence of the capnophilic rumen bacteriumMannheimia succiniciproducens.Nat. Biotechnol. 22: 1275–1281.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  8. Ciaramella, M., A. Napoli, and M. Rossi (2005) Another extreme genome: How to live at pH 0.Trends Microbiol. 13: 49–51.

    Article  CAS  Google Scholar 

  9. Forster, J., I. Famili, P. Fu, B. O. Palsson, and J. Nielson (2003) Genome-scale reconstruction of theSaccharomyces cerevisiae metabolic network.Genome Res. 13: 244–253.

    Article  CAS  Google Scholar 

  10. Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards, and B. O. Palsson (2002) Genome-scale metabolic model ofHelicobacter pylori 26695.J. Bacteriol. 184: 4582–4593.

    Article  CAS  Google Scholar 

  11. Varma, A., B. W. Boesch, and B. O. Palsson (1993) Stoichiometric interpretation ofEscherichia coli glucose catabolism under various oxygenation rates.Appl. Environ. Microbiol. 59: 2465–2473.

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  13. Edwards, J. S., R. U. Ibarra, and B. O. Palsson (2001)In silico predictions ofEscherichia coli metabolic capabilities are consistent with experimental data.Nat. Biotechnol. 19: 125–130.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  15. Varma, A., B. W. Boesch, and B. O. Palsson (1993) Biochemical production capabilities ofEscherichia coli.Biotechnol. Bioeng. 42: 59–73.

    Article  CAS  Google Scholar 

  16. Edwards, J. S. and B. O. Palsson (2000) Metabolic flux balance analysis and thein silico analysis ofEscherichia coli K-12 gene deletions.BMC Bioinformatics 1: 1.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    CAS  Google Scholar 

  21. Mahadevan, R. and B. O. Palsson (2005) Properties of metabolic networks: Structure versus function.Biophys. J. 88: L07-L09.

    Article  CAS  Google Scholar 

  22. DeRisi, J. L., V. R. Iyer, and P. O. Brown (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale.Science 278: 680–686.

    Article  CAS  Google Scholar 

  23. Patterson, S. D. and R. H. Aebersold (2003) Proteomics: The first decade and beyond.Nat. Genet. 33 Suppl: 311–323.

    Article  CAS  Google Scholar 

  24. Kell, D. B. (2004) Metabolomics and systems biology: making sense of the soup.Curr. Opin. Microbiol. 7: 296–307.

    Article  CAS  Google Scholar 

  25. Churchill, G. A. (2004) Using ANOVA to analyze microarray data.Biotechniques 37: 173–177.

    CAS  Google Scholar 

  26. Sharan, R., R. Elkon, and R. Shamir (2002) Cluster analysis and its applications to gene expression data.Ernst. Schering. Res. Found. Workshop 83–108.

  27. Ideker, T., V. Thorsson, J. A. Ranish, R. Christmas, J. Buhler, J. K. Eng, R. Bumgarner, D. R. Goodlett, R. Aebersold, and L. Hood (2001) Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.Science 292: 929–934.

    Article  CAS  Google Scholar 

  28. Burgard, A. P., E. V. Nikolaev, C. H. Schilling, and C. D. Maranas (2004) Flux coupling analysis of genome-scale metabolic network reconstructions.Genome Res. 14: 301–312.

    Article  CAS  Google Scholar 

  29. Oh, M. K. and J. C. Liao (2000) Gene expression profiling by DNA microarrays and metabolic fluxes inEscherichia coli.Biotechnol. Prog. 16: 278–286.

    Article  CAS  Google Scholar 

  30. Tao, H., R. Gonzalez, A. Martinez, M. Rodriguez, L. O. Ingram, J. F. Preston, and K. T. Shanmugam (2001) Engineering a homo-ethanol pathway inEscherichia coli: Increased glycolytic flux and levels of expression of glycolytic genes during xylose fermentation.J. Bacteriol. 183: 2979–2988.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  32. Patil, K. R. and J. Nielsen (2005) Uncovering transcriptional regulation of metabolism by using metabolic network topology.Proc. Natl. Acad. Sci. USA 102: 2685–2689.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  34. van der Heijden, R. T. J. M., J. J. Heijnen, C. Hellinga, B. Romein, and K. C. A. M. Luyben (1994) Linear constraint relations in biochemical reaction systems: II. Diagnosis and estimation of gross measurement errors.Biotechnol. Bioeng. 43: 11–20.

    Article  Google Scholar 

  35. Raghunathan, A. U., J. R. Perez-Correa, and L. T. Biegler (2003) Data reconciliation and parameter estimation in flux-balance analysis.Biotechnol. Bioeng. 84: 700–708.

    Article  CAS  Google Scholar 

  36. Mahadevan, R. and C. H. Schilling (2003) The effects of alternate optimal solutions in constraint-based genomescale metabolic models.Metab. Eng. 5: 264–276.

    Article  CAS  Google Scholar 

  37. Vallino, J. J. and G. Stephanopoulos (1993) Metabolic fluc distributions inCorynebacterium glutamicum during growth and lysine overproduction.Biotechnol. Bioeng. 41: 633–646.

    Article  CAS  Google Scholar 

  38. van Gulik, W. M., W. T. de Laat, J. L. Vinke, and J. J. Heijnen (2000) Application of metabolic flux analysis for the identification of metabolic bottlenecks in the biosynthesis of penicillin-G.Biotechnol. Bioeng. 68: 602–618.

    Article  Google Scholar 

  39. Schilling, C. H., J. S. Edwards, and B. O. Palsson (1999) Toward metabolic phenomics: Analysis of genomic data using flux balances.Biotechnol. Prog. 15: 288–295.

    Article  CAS  Google Scholar 

  40. Shimizu, H., N. Takiguchi, H. Tanaka, and S. Shioya (1999) A maximum production strategy of lysine based on a simplified model derived from a metabolic reaction network.Metab. Eng. 1: 299–308.

    Article  CAS  Google Scholar 

  41. Wiechert, W. (2001)13C metabolic flux analysis.Metab. Eng. 195–206.

  42. Marx, A., A. A. de Graaf, W. Wiechert, L. Eggeling, and H. Sahm (1996) Determination of the fluxes in central metabolism ofCorynebacterium glutamicum by NMR spectroscopy combined with metabolite balancing.Biotechnol. Bioeng. 49: 111–129.

    Article  CAS  Google Scholar 

  43. Dauner, M. and U. Sauer (2000) GC-MS analysis of amino acids rapidly provides rich information for isotopomer balancing.Biotechnol. Prog. 16: 642–649.

    Article  CAS  Google Scholar 

  44. Schmidt, K., M. Carlsen, J. Nielsen, and J. Villadsen (1997) Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices.Biotechnol. Bioeng. 55: 831–840.

    Article  CAS  Google Scholar 

  45. van Dien, S. J., T. Strovas, and M. E. Lidstrom (2003) Quantification of central metabolic fluxes in the facultative methylotroph methylobacterium extorquens AM1 using13C-label tracing and mass spectrometry.Biotechnol Bioeng. 84: 45–55.

    Article  CAS  Google Scholar 

  46. Wiechert, W., C. Siefke, A. A. de Graaf, and A. Marx (1997) Bidirectional reaction steps in metabolic networks: II. Flux estimation and statistical analysis.Biotechnol. Bioeng. 55: 118–135.

    Article  CAS  Google Scholar 

  47. Wiechert, W. and A. A. de Graaf (1997) Bidirectional reaction steps in metabolic networks: I. Modeling and simulation of carbon isotope labeling experiments.Biotechnol. Bioeng. 55: 101–117.

    Article  CAS  Google Scholar 

  48. Wittmann, C. and E. Heinzle (1999) Mass spectrometry for metabolic flux analysis.Biotechnol. Bioeng. 62: 739–750.

    Article  CAS  Google Scholar 

  49. Walsh, K. and D. E. Jr. Koshland (1984) Determination of flux through the branch point of two metabolic cycles. The tricarboxylic acid cycle and the glyoxylate shunt.J. Biol. Chem. 259: 9646–9654.

    CAS  Google Scholar 

  50. Park, S. M., M. I. Klapa, A. J. Sinskey, and G. N. Stephanopoulos (1999) Metabolite and isotopomer balancing in the analysis of metabolic cycles: II. Applications.Biotechnol. Bioeng. 62: 392–401.

    Article  CAS  Google Scholar 

  51. Wendisch, V. F., A. A. de Graaf, H. Sahm, and B. J. Eikmanns (2000) Quantitative determination of metabolic fluxes during coutilization of two carbon sources: Comparative analyses withCorynebacterium glutamicum during growth on acetate and/or glucose.J. Bacteriol. 182: 3088–3096.

    Article  CAS  Google Scholar 

  52. Petersen, S., A. A. de Graaf, L. Eggeling, M. Mollney, W. Wiechert, and H. Sahm (2000)In vivo quantification of parallel and bidirectional fluxes in the anaplerosis ofCorynebacterium glutamicum.Metab. Eng. 3: 195–206.

    Google Scholar 

  53. Wittmann, C., H. M. Kim, and E. Heinzle (2004) Metabolic network analysis of lysine producingCorynebacterium glutamicum at a miniaturized scale.Biotechnol. Bioeng. 87: 1–6.

    Article  CAS  Google Scholar 

  54. Sauer, U., D. R. Lasko, J. Fiaux, M. Hochuli, R. Glaser, T. Szyperski, K. Wuthrich, and J. E. Bailey (1999) Metabolic flux ratio analysis of genetic and environmental modulations ofEscherichia coli central carbon metabolism.J. Bacteriol. 181: 6679–6688.

    CAS  Google Scholar 

  55. Wahl, A., M. El Massaoudi, D. Schipper, W. Wiechert, and R. Takors (2004) Serial13C-based flux analysis of an L-phenylalanine-producingE. coli strain using a sensor reactor.Biotechnol. Prog. 20: 706–714.

    Article  CAS  Google Scholar 

  56. Sauer, U., V. Hatzimanikatis, J. E. Bailey, M. Hochuli, T. Szyperski, and K. Wuthrich (1997) Metabolic fluxes in riboflavin-producingBacillus subtilis.Nat. Biotechnol. 15: 448–452.

    Article  CAS  Google Scholar 

  57. Gombert, A. K., S. M. Moreirados, B. Christensen, and J. Nielsen (2001) Network identification and flux quantification in the central metabolism ofSaccharomyces cerevisiae under different conditions of glucose repression.J. Bacteriol. 183: 1441–1451.

    Article  CAS  Google Scholar 

  58. Christensen, B. and J. Nielsen (2000) Metabolic network analysis ofPenicillium chrysogenum using13C-labeled glucose.Biotechnol. Bioeng. 68: 652–659.

    Article  CAS  Google Scholar 

  59. Jensen, N. B. S., B. Christensen, J. Nielsen, and J. Villadsen (2002) The simultaneous biosynthesis and uptake of amino acids byLactococcus lactis studied by13C-labeling experiments.Biotechnol. Bioeng. 78: 11–16.

    Article  CAS  Google Scholar 

  60. Burgard, A. P. and C. D. Maranas (2001) Probing the performance limits of theEscherichia coli metabolic network subject to gene additions or deletions.Biotechnol. Bioeng. 74: 364–375.

    Article  CAS  Google Scholar 

  61. Carlson, R., D. Fell, and F. Sriene (2002) Metabolic pathway analysis of a recombinant yeast for rational strain development.Biotechnol. Bioeng. 79: 121–34.

    Article  CAS  Google Scholar 

  62. Fong, S. S. and B. O. Palsson (2004) Metabolic genedeletion strains ofEscherichia coli evolve to computationally predicted growth phenotypes.Nat. Genet. 36: 1056–1058.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  64. Pharkya, P., A. P. Burgard, and C. D. Maranas (2003) Exploring the overproduction of amino acids using the bilevel optimization framework OptKnock.Biotechnol. Bioeng. 84: 887–899.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  66. Wilson, E. K. (2005) Engineering cell-based factories.Chem. Eng. News 83: 41–44.

    Google Scholar 

  67. Broadbelt, L. J., S. M. Stark, and M. T. Klein (1994) Computer-generated pyrolysis modeling-on-the-fly generation of species, reactions, and rates.Ind. Eng. Chem. Res. 33: 790–799.

    Article  CAS  Google Scholar 

  68. Broadbelt, L. J., S. M. Stark, and M. T. Klein (1995) Termination of computer-generated reaction-mechanisms-species rank-based convergence criterion.Ind. Eng. Chem. Res. 34: 2566–2573.

    Article  CAS  Google Scholar 

  69. Broadbelt, L. J., S. M. Stark, and M. T. Klein (1996) Computer generated reaction modelling: Decomposition and encoding algorithms for determining species uniqueness.Comput. Chem. Eng. 20: 113–129.

    Article  CAS  Google Scholar 

  70. Hatzimanikatis, V., C. Li, J. A. Ionita, and L. J. Broadbelt (2004) Metabolic networks: Enzyme function and metabolite structure.Curr. Opin. Struct. Biol. 14: 300–306.

    Article  CAS  Google Scholar 

  71. Kanehisa, M., S. Goto, S. Kawashima, Y. Okuno, and M. Hattori (2004) The KEGG resource for deciphering the genome.Nucleic Acids Res. 32 Database issue: D277–D280.

    Google Scholar 

  72. Karp, P. D., M. Riley, M. Saier, I. T. Paulsen, S. M. Paley, and A. Pellegrini-Toole (2000) The EcoCyc and MetaCyc databases.Nucleic Acids Res. 28: 56–59.

    Article  CAS  Google Scholar 

  73. Krieger, C. J., P. Zhang, L. A. Mueller, A. Wang, S. Paley, M. Arnaud, J. Pick, S. Y. Rhee, and P. D. Karp (2004) MetaCyc: A multiorganism database of metabolic pathways and enzymes.Nucleic Acids Res. 32 Database issue: D438–D442.

    Google Scholar 

  74. Li, C., C. S. Henry, M. D. Jankowski, J. A. Ionita, V. Hatzimanikatis, and L. J. Broadbelt (2004) Computational discovery of biochemical routes to specialty chemicals.Chem. Eng. Sci. 59: 5051–5060.

    Article  CAS  Google Scholar 

  75. Hatzimanikatis, V., C. Li, J. A. Ionita, C. S. Henry, M. D. Jankowski, and L. J. Broadbelt (2005) Exploring the diversity of complex metabolic networks.Bioinformatics 21: 1603–1609.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  77. Komives, C. and R. S. Parker (2003) Bioreactor state estimation and control.Curr. Opin. Biotechnol. 14: 468–474.

    Article  CAS  Google Scholar 

  78. Covert, M. W. and B. O. Palsson (2002) Transcriptional regulation in constraints-based metabolic models ofEscherichia coli.J. Biol. Chem. 277: 28058–28064.

    Article  CAS  Google Scholar 

  79. Mahadevan, R., J. S. Edwards, and F. J. Doyle (2002) Dynamic flux balance analysis of diauxic growth inEscherichia coli.Biophysical J. 83: 1331–1340.

    Article  CAS  Google Scholar 

  80. Gadkar, K. G., F. J. Doyle, III, T. J. Crowley, and J. D. Varner (2003) Cybernetic model predictive control of a continuous bioreactor with cell recycle.Biotechnol Prog. 19: 1487–1497.

    Article  CAS  Google Scholar 

  81. Mahadevan, R. and F. J. Doyle (2003) On-line optimization of recombinant product in a fed-batch bioreactor.Biotechnol. Prog. 19: 639–646.

    Article  CAS  Google Scholar 

  82. Parekh, S., V. A. Vinci, and R. J. Strobel (2000) Improvement of microbial strains and fermentation processes.Appl. Microbiol. Biotechnol. 54: 287–301.

    Article  CAS  Google Scholar 

  83. Zhang, S., J. Chu, and Y. Zhuang (2004) A multi-scale study of industrial fermentation processes and their optimization.Adv. Biochem. Eng. Biotechnol. 87: 97–150.

    CAS  Google Scholar 

  84. Gadkar, K. G., F. J. Doyle, J. S. Edwards, and R. Mahadevan (2005) Estimating optimal profiles of genetic alterations using constraint-based models.Biotechnol. Bioeng. 89: 243–251.

    Article  CAS  Google Scholar 

  85. Lovley, D. R. (2003) Cleaning up with genomics: Applying molecular biology to bioremediation.Nat. Rev. Microbiol. 1: 35–44.

    Article  CAS  Google Scholar 

  86. Beard, D. A. and H. Qian (2005) Thermodynamic-based computational profiling of cellular regulatory control in hepatocyte metabolism.Am. J. Physiol. Endocrinol. Metab. 288: E633-E644.

    Article  CAS  Google Scholar 

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  1. Genomatica, Inc., 5405 Morehouse Drive, Suite 210, 92130, San Diego, CA, USA

    Radhakrishnan Mahadevan, Anthony P. Burgard, Iman Famili, Steve Van Dien & Christophe H. Schilling

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  1. Radhakrishnan Mahadevan
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  2. Anthony P. Burgard
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Mahadevan, R., Burgard, A.P., Famili, I. et al. Applications of metabolic modeling to drive bioprocess development for the production of value-added chemicals. Biotechnol. Bioprocess Eng. 10, 408–417 (2005). https://doi.org/10.1007/BF02989823

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Keywords

  • bioprocess development
  • constraint-based modeling
  • metabolic engineering
  • SimPheny®