Biotechnology and Bioprocess Engineering:BBE

  • 제10권5호
  • /
  • Pages.408-417
  • /
  • 2005
  • /
  • 1226-8372(pISSN)
  • /
  • 1976-3816(eISSN)
한국생물공학회 (The Korean Society for Biotechnology and Bioengineering)
권호 표지

Applications of Metabolic Modeling to Drive Bioprocess Development for the Production of Value-added Chemicals

  • 발행 : 2005.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  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 https://doi.org/10.1038/nrmicro1023
  2. Edwards, J. S. and B. O. Palsson (2000) The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities. Proc. Natl. Acad. Sci. USA 97: 5528-5533 https://doi.org/10.1073/pnas.97.10.5528
  3. Reed, J. L., T. D. Vo, C. H. Schilling, and B. Palsson (2003) Escherichia coli iJR904: An expanded genomescale model of E. coli K-12. Genome Biol. 4: R54.1-R54.12.
  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 https://doi.org/10.1073/pnas.2235812100
  5. Dauner, M. and U. Sauer (2001) Stoichiometric growth model for riboflavin-producing Bacillus subtilis. Biotechnol. Bioeng. 76: 132-143 https://doi.org/10.1002/bit.1153
  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 bacterium Mannheimia succiniciproducens. Nat. Biotechnol. 22: 1275-1281 https://doi.org/10.1038/nbt1010
  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 https://doi.org/10.1038/nature02456
  8. Ciaramella, M., A. Napoli, and M. Rossi (2005) Another extreme genome: How to live at pH 0. Trends Microbiol. 13: 49-51 https://doi.org/10.1016/j.tim.2004.12.001
  9. Forster, J., I. Famili, P. Fu, B. O. Palsson, and J. Nielsen (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13: 244-253 https://doi.org/10.1101/gr.234503
  10. Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards, and B. O. Palsson (2002) Genome-scale metabolic model of Helicobacter pylori 26695. J. Bacteriol. 184: 4582-4593 https://doi.org/10.1128/JB.184.16.4582-4593.2002
  11. Varma, A., B. W. Boesch, and B. O. Palsson (1993) Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl. Environ. Microbiol. 59: 2465-2473
  12. Varma, A. and B. O. Palsson (1994) Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 60: 3724-3731
  13. Edwards, J. S., R. U. Ibarra, and B. O. Palsson (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat. Biotechnol. 19: 125-130 https://doi.org/10.1038/84379
  14. Ibarra, R. U., J. S. Edwards, and B. O. Palsson (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420: 186-189 https://doi.org/10.1038/nature01149
  15. Varma, A., B. W. Boesch, and B. O. Palsson (1993) Biochemical production capabilities of Escherichia coli. Biotechnol. Bioeng. 42: 59-73 https://doi.org/10.1002/bit.260420109
  16. Edwards, J. S. and B. O. Palsson (2000) Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics 1:1
  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 https://doi.org/10.1073/pnas.232349399
  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 https://doi.org/10.1073/pnas.0406346102
  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 https://doi.org/10.1038/nature02636
  20. Segre, D., A. Deluna, G. M. Church, and R. Kishony (2005) Modular epistasis in yeast metabolism. Nat. Genet. 37: 77-83 https://doi.org/10.1038/ng1489
  21. Mahadevan, R. and B. O. Palsson (2005) Properties of metabolic networks: Structure versus function. Biophys. J. 88: L07-L09 https://doi.org/10.1529/biophysj.104.055723
  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 https://doi.org/10.1126/science.278.5338.680
  23. Patterson, S. D. and R. H. Aebersold (2003) Proteomics: The first decade and beyond. Nat. Genet. 33 Suppl: 311-323 https://doi.org/10.1038/ng1106
  24. Kell, D. B (2004) Metabolomics and systems biology: making sense of the soup. Curr. Opin. Microbiol. 7: 296-307 https://doi.org/10.1016/j.mib.2004.04.012
  25. Churchill, G. A. (2004) Using ANOVA to analyze microarray data. Biotechniques 37: 173-177
  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 https://doi.org/10.1126/science.292.5518.929
  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 https://doi.org/10.1101/gr.1926504
  29. Oh, M. K. and J. C. Liao (2000) Gene expression profiling by DNA microarrays and metabolic fluxes in Escherichia coli. Biotechnol. Prog. 16: 278-286 https://doi.org/10.1021/bp000002n
  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 in Escherichia coli: Increased glycolytic flux and levels of expression of glycolytic genes during xylose fermentation. J. Bacteriol. 183: 2979-2988 https://doi.org/10.1128/JB.183.10.2979-2988.2001
  31. Akesson, M., J. Forster, and J. Nielsen (2004) Integration of gene expression data into genome-scale metabolic models. Metab. Eng. 6: 285-293 https://doi.org/10.1016/j.ymben.2003.12.002
  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 https://doi.org/10.1073/pnas.0406811102
  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 https://doi.org/10.1006/jtbi.2001.2405
  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 https://doi.org/10.1002/bit.260430104
  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 https://doi.org/10.1002/bit.10823
  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 https://doi.org/10.1016/j.ymben.2003.09.002
  37. Vallino, J. J. and G. Stephanopoulos (1993) Metabolic fluc distributions in Corynebacterium glutamicum during growth and lysine overproduction. Biotechnol. Bioeng. 41: 633-646 https://doi.org/10.1002/bit.260410606
  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 https://doi.org/10.1002/(SICI)1097-0290(20000620)68:6<602::AID-BIT3>3.0.CO;2-2
  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 https://doi.org/10.1021/bp9900357
  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 https://doi.org/10.1006/mben.1999.0127
  41. Wiechert, W (2001) $^{13}C$ 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 of Corynebacterium glutamicum by NMR spectroscopy combined with metabolite balancing. Biotechnol. Bioeng. 49: 111-129 https://doi.org/10.1002/(SICI)1097-0290(19960120)49:2<111::AID-BIT1>3.0.CO;2-T
  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 https://doi.org/10.1021/bp000058h
  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 https://doi.org/10.1002/(SICI)1097-0290(19970920)55:6<831::AID-BIT2>3.0.CO;2-H
  45. van Dien, S. J., T. Strovas, and M. E. Lidstrom (2003) Quantification of central metabolic fluxes in the facultative methylotroph methylobacterium extorquens AM1 using $^{13}C$-label tracing and mass spectrometry. Biotechnol. Bioeng. 84: 45-55 https://doi.org/10.1002/bit.10745
  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 https://doi.org/10.1002/(SICI)1097-0290(19970705)55:1<118::AID-BIT13>3.0.CO;2-I
  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 https://doi.org/10.1002/(SICI)1097-0290(19970705)55:1<101::AID-BIT12>3.0.CO;2-P
  48. Wittmann, C. and E. Heinzle (1999) Mass spectrometry for metabolic flux analysis. Biotechnol. Bioeng. 62: 739-750 https://doi.org/10.1002/(SICI)1097-0290(19990320)62:6<739::AID-BIT13>3.0.CO;2-E
  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
  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 https://doi.org/10.1002/(SICI)1097-0290(19990220)62:4<392::AID-BIT2>3.0.CO;2-S
  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 with Corynebacterium glutamicum during growth on acetate and/or glucose. J. Bacteriol. 182: 3088-3096 https://doi.org/10.1128/JB.182.11.3088-3096.2000
  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 of Corynebacterium glutamicum. Metab. Eng. 3: 195-206 https://doi.org/10.1006/mben.2001.0187
  53. Wittmann, C., H. M. Kim, and E. Heinzle (2004) Metabolic network analysis of lysine producing Corynebacterium glutamicum at a miniaturized scale. Biotechnol. Bioeng. 87: 1-6 https://doi.org/10.1002/bit.20103
  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 of Escherichia coli central carbon metabolism. J. Bacteriol. 181: 6679-6688
  55. Wahl, A., M. El Massaoudi, D. Schipper, W. Wiechert, and R. Takors (2004) Serial $^{13}C$-based flux analysis of an L-phenylalanine-producing E. coli strain using a sensor reactor. Biotechnol. Prog. 20: 706-714 https://doi.org/10.1021/bp0342755
  56. Sauer, U., V. Hatzimanikatis, J. E. Bailey, M. Hochuli, T. Szyperski, and K. Wuthrich (1997) Metabolic fluxes in riboflavin-producing Bacillus subtilis. Nat. Biotechnol. 15: 448-452 https://doi.org/10.1038/nbt0597-448
  57. Gombert, A. K., S. M. Moreira dos, B. Christensen, and J. Nielsen (2001) Network identification and flux quantification in the central metabolism of Saccharomyces cerevisiae under different conditions of glucose repression. J. Bacteriol. 183: 1441-1451 https://doi.org/10.1128/JB.183.4.1441-1451.2001
  58. Christensen, B. and J. Nielsen (2000) Metabolic network analysis of Penicillium chrysogenum using $^{13}C$-labeled glucose. Biotechnol. Bioeng. 68: 652-659 https://doi.org/10.1002/(SICI)1097-0290(20000620)68:6<652::AID-BIT8>3.0.CO;2-J
  59. Jensen, N. B. S., B. Christensen, J. Nielsen, and J. Villadsen (2002) The simultaneous biosynthesis and uptake of amino acids by Lactococcus lactis studied by $^{13}C$-labeling experiments. Biotechnol. Bioeng. 78: 11-16 https://doi.org/10.1002/bit.10211
  60. Burgard, A. P. and C. D. Maranas (2001) Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions. Biotechnol. Bioeng. 74: 364-375 https://doi.org/10.1002/bit.1127
  61. Carlson, R., D. Fell, and F. Srienc (2002) Metabolic pathway analysis of a recombinant yeast for rational strain development. Biotechnol. Bioeng. 79: 121-34 https://doi.org/10.1002/bit.10305
  62. Fong, S. S. and B. O. Palsson (2004) Metabolic genedeletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat. Genet. 36: 1056-1058 https://doi.org/10.1038/ng1432
  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 https://doi.org/10.1002/bit.10803
  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 https://doi.org/10.1002/bit.10857
  65. Alper, H., Y. S. Jin, J. F. Moxley, and G. Stephanopoulos (2005) Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. 7: 155-164 https://doi.org/10.1016/j.ymben.2004.12.003
  66. Wilson, E. K. (2005) Engineering cell-based factories. Chem. Eng. News 83: 41-44
  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 https://doi.org/10.1021/ie00028a003
  68. Broadbelt, L. J., S. M. Stark, and M. T. Klein (1995) Termination of computer-generated reaction-mechanismsspecies rank-based convergence criterion. Ind. Eng. Chem. Res. 34: 2566-2573 https://doi.org/10.1021/ie00047a003
  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 https://doi.org/10.1016/0098-1354(94)00009-D
  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 https://doi.org/10.1016/j.sbi.2004.04.004
  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 https://doi.org/10.1093/nar/gkh063
  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 https://doi.org/10.1093/nar/28.1.56
  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 https://doi.org/10.1093/nar/gkh100
  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 https://doi.org/10.1016/j.ces.2004.09.021
  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 https://doi.org/10.1093/bioinformatics/bti213
  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 https://doi.org/10.1101/gr.2872004
  77. Komives, C. and R. S. Parker (2003) Bioreactor state estimation and control. Curr. Opin. Biotechnol. 14: 468-474 https://doi.org/10.1016/j.copbio.2003.09.001
  78. Covert, M. W. and B. O. Palsson (2002) Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J. Biol. Chem. 277: 28058-28064 https://doi.org/10.1074/jbc.M201691200
  79. Mahadevan, R., J. S. Edwards, and F. J. Doyle (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophysical J. 83: 1331-1340 https://doi.org/10.1016/S0006-3495(02)73903-9
  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 https://doi.org/10.1021/bp025776d
  81. Mahadevan, R. and F. J. Doyle (2003) On-line optimization of recombinant product in a fed-batch bioreactor. Biotechnol. Prog. 19: 639-646 https://doi.org/10.1021/bp025546z
  82. Parekh, S., V. A. Vinci, and R. J. Strobel (2000) Improvement of microbial strains and fermentation processes. Appl. Microbiol. Biotechnol. 54: 287-301 https://doi.org/10.1007/s002530000403
  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
  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 https://doi.org/10.1002/bit.20349
  85. Lovley, D. R. (2003) Cleaning up with genomics: Applying molecular biology to bioremediation. Nat. Rev. Microbiol. 1: 35-44 https://doi.org/10.1038/nrmicro731
  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 https://doi.org/10.1152/ajpendo.00239.2004