Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
권호 표지
DOI QR코드

DOI QR Code

Review of Current Approaches for Implementing Metabolic Reconstruction

  • Kim, Do-Gyun (Department of Biosystems Machinery Engineering, Chungnam National University) ;
  • Seo, Sung-Won (Department of Animal Biosystem Science, Chungnam National University) ;
  • Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, Chungnam National University) ;
  • Lohumi, Santosh (Department of Biosystems Machinery Engineering, Chungnam National University) ;
  • Hong, Soon-jung (Rural Human Resource Development Center, Rural Development Administration) ;
  • Lee, Wang-Hee (Department of Biosystems Machinery Engineering, Chungnam National University)
  • Received : 2017.11.28
  • Accepted : 2018.02.22
  • Published : 2018.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

Background: Metabolic modeling has been an essential tool in metabolic reconstruction, which has dramatically advanced in the last decades as a part of systems biology. At present, the protocol for metabolic reconstruction has been systematically established, and it provides the basis for the analysis of complex systems, which has been limited in the past. Therefore, metabolic reconstruction can be adapted to analyze agricultural systems whose metabolic data has been accumulated recently. Purpose: The aim of this review is to suggest the suitability of metabolic modeling for understanding agricultural metabolic data and to encourage the potential use of this modeling in the field of agriculture. Review: We reviewed the procedure of metabolic reconstruction using computational modeling with applicable strategies and software tools. Additionally, we presented the initial attempts of metabolic reconstruction in the field of agriculture and proposed further applications.

Keywords

References

  1. Altschul, S. F., W. Gish, W. Miller, E. W. Myers and D. J. Lipman. 1990. Basic local alignment search tool. Journal of Molecular Biology 215(3): 403-410. https://doi.org/10.1006/jmbi.1990.9999
  2. Bairoch, A. 1994. The ENZYME data bank. Nucleic Acids Research 22(17): 3626-3627. https://doi.org/10.1093/nar/22.17.3626
  3. Bajwa, S. G., P. Bajcsy, P. Groves and L. E. Tian. 2004. Hyperspectral image data mining for band selection in agricultural applications. Transactions of the ASAE 47(3): 895-907. https://doi.org/10.13031/2013.16087
  4. Bates, J. T., D. Chivian and A. P. Arkin. 2011. GLAMM: genome-linked application for metabolic maps. Nucleic acids research 39 (suppl_2): W400-W405. https://doi.org/10.1093/nar/gkr433
  5. Becker, S. A., A. M. Feist, M. L. Mo, G. Hannum, B. O. Palsson and M. J. Herrgard. 2007. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nature Protocols 2(3): 727-738. https://doi.org/10.1038/nprot.2007.99
  6. Becker, S. A. and B. O. Palsson. 2008. Context-specific metabolic networks are consistent with experiments. PLoS Computational Biology 4(5): e1000082. https://doi.org/10.1371/journal.pcbi.1000082
  7. Beste, D. J., T. Hooper, G. Stewart, B. Bonde, C. Avignone-Rossa, M. E. Bushell, P. Wheeler, S. Klamt, A. M. Kierzek, and J. McFadden. 2007. GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biology 8(5): R89. https://doi.org/10.1186/gb-2007-8-5-r89
  8. Bodor, Z., A. I. Fazakas, E. Kovcs, S. Lnyi and B. Albert. 2014. Systems biology and metabolic engineering for obtaining E. coli mutants capable to produce succinate from renewable resources. Romanian Biotechnological Letters 19(4): 9633-9644.
  9. Boghigian, B. A., J. Armando, D. Salas and B. A. Pfeifer. 2012. Computational identification of gene over-expression targets for metabolic engineering of taxadiene production. Applied Microbiology and Biotechnology 93(5): 2063-2073. https://doi.org/10.1007/s00253-011-3725-1
  10. Bombarely, A., N. Menda, I. Y. Tecle, R. M. Buels, S. Strickler, T. Fischer-York, A. Pujar, J. Leto, J. Gosselin and L. A. Mueller. 2011. The sol genomics network (solgenomics.net): Growing tomatoes using Perl. Nucleic Acids Research 39 (suppl_1): D1149-D1155. https://doi.org/10.1093/nar/gkq866
  11. Bonarius, H. P. J., G. Schmid and J. Tramper. 1997. Flux analysis of underdetermined metabolic networks: The quest for the missing constraints. Trends in Biotechnology 15(8): 308-314. https://doi.org/10.1016/S0167-7799(97)01067-6
  12. Burgard, A. P., P. Pharkya and C. D. Maranas. 2003. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering 84(6): 647-657. https://doi.org/10.1002/bit.10803
  13. Caspi, R., H. Foerster, C. A. Fulcher, P. Kaipa, M. Krummenacker, M. Latendresse, S. Paley, S. Y. Rhee, A. G. Shearer, C. Tissier, T. C. Walk, P. Zhang and P. D. Karp. 2007. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Research 36 (suppl_1): D623-D631. https://doi.org/10.1093/nar/gkm900
  14. Chou, I. C. and E. O. Voit. 2009. Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Mathematical Biosciences 219(2): 57-83. https://doi.org/10.1016/j.mbs.2009.03.002
  15. Copeland, W. B., B. A. Bartley, D. Chandran, M. Galdzicki, K. H. Kim, S. C. Sleight, C. D. Maranas and H. M. Sauro. 2012. Computational tools for metabolic engineering. Metabolic Engineering 14(3): 270-280. https://doi.org/10.1016/j.ymben.2012.03.001
  16. Dharmawardhana, P., L. Ren, V. Amarasinghe, M. Monaco, J. Thomason, D. Ravenscroft, S. McCouch, D. Ware and P. Jaiswal. 2013. A genome scale metabolic network for rice and accompanying analysis of tryptophan, auxin and serotonin biosynthesis regulation under biotic stress. Rice 6(1): 15. https://doi.org/10.1186/1939-8433-6-15
  17. Droste, P., K. Noh and W. Wiechert. 2013. Omix-A visualization tool for metabolic networks with highest usability and customizability in focus. Chemie Ingenieur Technik 85(6): 849-862. https://doi.org/10.1002/cite.201200234
  18. Durot, M., P. Y. Bourguignon and V. Schachter. 2009. Genome-scale models of bacterial metabolism: Reconstruction and applications. FEMS Microbiology Reviews 33(1): 164-190. https://doi.org/10.1111/j.1574-6976.2008.00146.x
  19. Ebrahim, A., J. A. Lerman, B. O. Palsson and D. R. Hyduke. 2013. COBRApy: Constraints-based reconstruction and analysis for Python. BMC Systems Biology 7: 74. https://doi.org/10.1186/1752-0509-7-74
  20. Edwards, J. S., M. Covert and B. Palsson. 2002. Metabolic modelling of microbes: The flux-balance approach. Environmental Microbiology 4(3): 133-140. https://doi.org/10.1046/j.1462-2920.2002.00282.x
  21. Edwards, J. S. and B. O. Palsson. 2000. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proceedings of the National Academy of Sciences of the United States of America 97(10): 5528-5533. https://doi.org/10.1073/pnas.97.10.5528
  22. Faraji, M., L. L. Fonseca, L. Escamilla-Trevino, R. A. Dixon and E. O. Voit. 2015. Computational inference of the structure and regulation of the lignin pathway in Panicum virgatum. Biotechnology for Biofuels 8: 151. https://doi.org/10.1186/s13068-015-0334-8
  23. Feist, A. M., M. J. Herrgrd, I. Thiele, J. L. Reed and B. O. Palsson. 2009. Reconstruction of biochemical networks in microorganisms. Nature Reviews Microbiology 7(2): 129-143. https://doi.org/10.1038/nrmicro1949
  24. Gombert, A. K. and J. Nielsen. 2000. Mathematical modelling of metabolism. Current Opinion in Biotechnology 11(2): 180-186. https://doi.org/10.1016/S0958-1669(00)00079-3
  25. Grafahrend-Belau, E., A. Junker, A. Eschenroder, J. Müller, F. Schreiber and B. H. Junker. 2013. Multiscale metabolic modeling: Dynamic flux balance analysis on a whole plant scale. Plant Physiology 163(2): 637-647. https://doi.org/10.1104/pp.113.224006
  26. Grafahrend-Belau, E., C. Klukas, B. H. Junker and F. Schreiber. 2009a. FBA-SimVis: Interactive visualization of constraint-based metabolic models. Bioinformatics 25(20): 2755-2757. https://doi.org/10.1093/bioinformatics/btp408
  27. Grafahrend-Belau, E., F. Schreiber, D. Koschutzki and B. H. Junker. 2009b. Flux balance analysis of barley seeds: a computational approach to study systemic properties of central metabolism. Plant Physiology 149(1): 585-598. https://doi.org/10.1104/pp.108.129635
  28. Haefner, J. W. 1996. Modeling Biological Systems: Principles and Applications. London, UK: Chapman & Hall
  29. Hanigan, M. D. and R. L. Baldwin. 1994. A mechanistic model of mammary gland metabolism in the lactating cow. Agricultural Systems 45(4): 369-419. https://doi.org/10.1016/0308-521X(94)90132-Y
  30. Hengenius, J. B., M. Gribskov, A. E. Rundell, C. C. Fowlkes and D. M. Umulis. 2011. Analysis of gap gene regulation in a 3D organism-scale model of the Drosophila melanogaster embryo. PLoS ONE 6(11): e26797. https://doi.org/10.1371/journal.pone.0026797
  31. Henry, C. S., M. DeJongh, A. A. Best, P. M. Frybarger, B. Linsay and R. L. Stevens. 2010. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature Biotechnology 28(9): 977-982. https://doi.org/10.1038/nbt.1672
  32. Hillier, L. D. W., W. Miller, E. Birney, W. Warren, R. C. Hardison, C. P. Ponting, P. Bork, D. W. Burt, M. A. M. Groenen, M. E. Delany and J. B. Dodgson. 2004. Sequencing and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432(7018): 695-716. https://doi.org/10.1038/nature03154
  33. Hoppe, A., S. Hoffmann, A. Gerasch, C. Gille and H.-G. Holzhutter. 2011. FASIMU: flexible software for flux-balance computation series in large metabolic networks. BMC Bioinformatics 12: 28.
  34. Hucka, M., A. Finney, H. M. Sauro, H. Bolouri, J. C. Doyle, H. Kitano, A. P. Arkin, B. J. Bornstein, D. Bray, A. Cornish-Bowden, A. A. Cuellar, S. Dronov, E. D. Gilles, M. Ginkel, V. Gor, I. I. Goryanin, W. J. Hedley, T. C. Hodgman J.-H. Hofmeyr P. J. Hunter N. S. Juty J. L. Kasberger A. Kremling U. Kummer N. Le Novre L. M. Loew D. Lucio P. Mendes E. Minch E. D. Mjolsness Y. Nakayama M. R. Nelson P. F. Nielsen T. Sakurada J. C. Schaff B. E. Shapiro T. S. Shimizu H. D. Spence J. Stelling K. Takahashi M. Tomita J. Wagner and J. Wang. 2003. The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 19(4): 524-531. https://doi.org/10.1093/bioinformatics/btg015
  35. Hwang, K. and K. S. Fu. 1983. Integrated computer architectures for image processing and database management. Computer 16(1): 51-60. https://doi.org/10.1109/MC.1983.1654166
  36. Junker, B. H., C. Klukas and F. Schreiber. 2006. VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics 7: 109.
  37. Kanehisa, M. and S. Goto. 2000. KEGG: Kyoto Encyclopaedia of Genes and Genomes. Nucleic Acids Research 28(1): 27-30. https://doi.org/10.1093/nar/28.1.27
  38. Kim, D.G., B.K. Cho and W.H. Lee. 2016a. A novel approach in analyzing agriculture and food systems: Review of modeling and its applications. Korean Journal of Agricultural Science 43(2): 163-175. https://doi.org/10.7744/KJOAS.20160019
  39. Kim, W. S., S. Y. Lee, H. S. Park, W. K. Baik, J. H. Lee and S. Seo. 2010. Reconstruction of metabolic pathway for the chicken genome. Korean Journal of Poultry Science 37(3): 275-282 (In Korean, with English abstract). https://doi.org/10.5536/KJPS.2010.37.3.275
  40. Kim, W. S., H. S. Park and S. W. Seo. 2016b. Global metabolic reconstruction and metabolic gene evolution in the cattle genome. PLoS ONE 11(3): e0150974. https://doi.org/10.1371/journal.pone.0150974
  41. Kim, W. and S. Seo. 2012. Sequencing of the cattle genome toward finding ways to increase feed efficiency of cattle. Journal of Animal and Veterinary Advances 11(17): 3223-3227. https://doi.org/10.3923/javaa.2012.3223.3227
  42. King, Z. A., A. Dräger, A. Ebrahim, N. Sonnenschein, N. E. Lewis and B. O. Palsson. 2015. Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS Computational Biology 11(8): e1004321. https://doi.org/10.1371/journal.pcbi.1004321
  43. Kitano, H. 2002. Systems biology: A brief overview. Science 295 (5560): 1662-1665. https://doi.org/10.1126/science.1069492
  44. Klanchui, A., C. Khannapho, A. Phodee, S. Cheevadhanarak and A. Meechai. 2012. iAK692: A genome-scale metabolic model of Spirulina platensis C1. BMC Systems Biology 6(1): 71.
  45. Kumar, A., P. F. Suthers and C. D. Maranas. 2012. MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases. BMC Bioinformatics 13: 6. https://doi.org/10.1186/1471-2105-13-6
  46. Lakshmanan M., G. Koh, B. K. Chung and D. Y. Lee. 2012. Software applications for flux balance analysis. Briefings in Bioinformatics 15(1): 180-122.
  47. Lakshmanan, M., Z. Zhang, B. Mohanty, J.-Y. Kwon, H.-Y. Choi, H.-J. Nam, D.-I. Kim and D.-Y. Lee. 2013. Elucidating rice cell metabolism under flooding and drought stresses using flux-based modeling and analysis. Plant Physiology 162(4): 2140-2150. https://doi.org/10.1104/pp.113.220178
  48. Lee, W.H. and M. R. Okos. 2016. Model-based analysis of IGF-1 effect on osteoblast and osteoclast regulation in bone turnover. Journal of Biological Systems 24(1): 63-89. https://doi.org/10.1142/S0218339016500042
  49. Lee, Y. and E. O. Voit. 2010. Mathematical modeling of monolignol biosynthesis in Populus xylem. Mathematical Biosciences 228(1): 78-89. https://doi.org/10.1016/j.mbs.2010.08.009
  50. Lemosquet, S., G. Raggio, G. E. Lobley, H. Rulquin, J. Guinard-Flament and H. Lapierre. 2009. Whole-body glucose metabolism and mammary energetic nutrient metabolism in lactating dairy cows receiving digestive infusions of casein and propionic acid. Journal of Dairy Science 92(12): 6068-6082. https://doi.org/10.3168/jds.2009-2018
  51. Liu, L., R. Agren, S. Bordel and J. Nielsen. 2010. Use of genome-scale metabolic models for understanding microbial physiology. FEBS Letters 584(12): 2556-2564. https://doi.org/10.1016/j.febslet.2010.04.052
  52. Maglott, D., K. Ostell, K. D. Pruitt and T. Tatusova. 2004. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Research 33 (suppl_1): D54-D58. https://doi.org/10.1093/nar/gki031
  53. Mahadevan, R., J. S. Edwards and F. J. Doyle. 2002. Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophysical Journal 83(3): 1331-1340. https://doi.org/10.1016/S0006-3495(02)73903-9
  54. Mahadevan, R. and C. H. Schilling. 2003. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metabolic Engineering 5(4): 264-276. https://doi.org/10.1016/j.ymben.2003.09.002
  55. Martin, O. and D. Sauvant. 2007. Dynamic model of the lactating dairy cow metabolism. Animal 1(8): 1143-1166. https://doi.org/10.1017/S1751731107000377
  56. McCloskey, D., B. O. Palsson and A. M. Feist. 2013. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Molecular Systems Biology 9(1): 661.
  57. Montgomery, D. C. 2008. Design and Analysis of Experiments. New York, USA: John Wiley & Sons.
  58. Nakahigashi, K., Y. Toya, N. Ishii, T. Soga, M. Hasegawa, H. Watanabe, Y. Takai, M. Honma, H. Mori and M. Tomita. 2009. Systematic phenome analysis of Escherichia coli multiple-knockout mutants reveals hidden reactions in central carbon metabolism. Molecular Systems Biology 5(1): 306. https://doi.org/10.1038/msb.2009.65
  59. Orth, J. D. and B. Palsson. 2010. Systematizing the generation of missing metabolic knowledge. Biotechnology and Bioengineering 107(3): 403-412. https://doi.org/10.1002/bit.22844
  60. Orth, J. D., I. Thiele and B. Palsson. 2010. What is flux Balance Analysis?. Nature Biotechnology 28(3): 245-248. https://doi.org/10.1038/nbt.1614
  61. Ouyang, S., W. Zhu, J. Hamilton, H. Lin, M. Campbell, K. Childs, F. Thibaud-Nissen, R. L. Malek, Y. Lee, L. Zheng, J. Orvis, B. Haas, J. Wortman and R. C. Buell. 2006. The TIGR rice genome annotation resource: improvements and new features. Nucleic Acids Research 35 (suppl_1): D883-D887.
  62. Palsson, B. 2006. Systems Biology: Properties of Reconstructed Networks. New York, USA: Cambridge University Press.
  63. Poolman, M. G., S. Kundu, R. Shaw and D. a Fell. 2013. Responses to light intensity in a genome-scale model of rice metabolism. Plant Physiology 162(2): 1060-1072. https://doi.org/10.1104/pp.113.216762
  64. Reed, J. L. and B. O. Palsson. 2003. Thirteen years of building constraint-based in silico models of Escherichia coli. Journal of Bacteriology 185(9): 2692-2699. https://doi.org/10.1128/JB.185.9.2692-2699.2003
  65. Resendis-Antonio, O., J. L. Reed, S. Encarnacion, J. Collado-Vides and B. Palsson. 2007. Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli. PLoS Computational Biology 3(10): 1887-1895.
  66. Rocha, I., P. Maia, P. Evangelista, P. Vilaca, S. Soares, J. P. Pinto, J. Nielsen, K. R. Patil, E. C. Ferreira and M. Rocha. 2010. OptFlux: an open-source software platform for in silico metabolic engineering. BMC Systems Biology 4(1): 45. https://doi.org/10.1186/1752-0509-4-45
  67. Ruppin, E., J. A. Papin, L. F. de Figueiredo and S. Schuster. 2010. Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks. Current Opinion in Biotechnology 21(4): 502-510. https://doi.org/10.1016/j.copbio.2010.07.002
  68. Saha, R., P. F. Suthers and C. D. Maranas. 2011. Zea mays irs1563: A comprehensive genome-scale metabolic reconstruction of maize metabolism. PLoS ONE 6 (7): e21784. https://doi.org/10.1371/journal.pone.0021784
  69. Sasidharan, K., T. Soga, M. Tomita and D. B. Murray. 2012. A yeast metabolite extraction protocol optimised for time-series analyses. PLoS ONE 7 (8): e44283. https://doi.org/10.1371/journal.pone.0044283
  70. Schellenberger, J., J. O. Park, T. M. Conrad and B. O. Palsson. 2010. BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 11: 213. https://doi.org/10.1186/1471-2105-11-213
  71. Schellenberger, J., I. Thiele, J. D. Orth, R. Que, R. M. T. Fleming, I. Thiele, J. D. Orth, A. M. Feist, D. C. Zielinski, A. Bordbar, N. E. Lewis, S. Rahmanian, J. Kang, D. R. Hyduke and B. O. Palsson. 2012. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protocols 6(9): 1290-1307. https://doi.org/10.1038/nprot.2011.308
  72. Schilling, C. H., D. Letscher and B. O. Palsson. 2000. Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. Journal of Theoretical Biology 203(3): 229-248. https://doi.org/10.1006/jtbi.2000.1073
  73. Schomburg, I., A. Chang, O. Hofmann, C. Ebeling, F. Ehrentreich and D. Schomburg. 2002. BRENDA: A resource for enzyme data and metabolic information. Trends in Biochemical Sciences 27(1): 54-56. https://doi.org/10.1016/S0968-0004(01)02027-8
  74. Schuster, S., T. Dandekar and D. A. Fell. 1999. Detection of elementary flux modes in biochemical networks: A promising tool for pathway analysis and metabolic engineering. Trends in Biotechnology 17(2): 53-60. https://doi.org/10.1016/S0167-7799(98)01290-6
  75. Seaver, S. M. D., C. S. Henry and A. D. Hanson. 2012. Frontiers in metabolic reconstruction and modeling of plant genomes. Journal of Experimental Botany 63(6): 2247-2258. https://doi.org/10.1093/jxb/err371
  76. Seo, S., D. M. Larkin and J. J. Loor. 2013. Cattle genomics and its implications for future nutritional strategies for dairy cattle. Animal 7 (s1): 172-183. https://doi.org/10.1017/S1751731111002588
  77. Seo, S. W., W. K. Paek and J. H. Lee. 2009. Recent status of chicken genome researches. Korean Journal of Poultry Science 36(2): 111-115 (In Korean, with English abstract). https://doi.org/10.5536/KJPS.2009.36.2.111
  78. Shlomi, T., O. Berkman and E. Ruppin. 2005. Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proceedings of the National Academy of Sciences of the United States of America 102 (21): 7695-7700. https://doi.org/10.1073/pnas.0406346102
  79. Simons, M., R. Saha, L. Guillard, G. Clment, P. Armengaud, R. Canas, C. D. Maranas, P. J. Lea and B. Hirel. 2014. Nitrogen-use efficiency in maize (Zea mays L.): From 'omics' studies to metabolic modelling. Journal of Experimental Botany 65(19): 5657-5671. https://doi.org/10.1093/jxb/eru227
  80. Stefanovski, D., P. J. Moate and R. C. Boston. 2003. WinSAAM: a windows-based compartmental modeling system. Metabolism 52(9): 1153-1166. https://doi.org/10.1016/S0026-0495(03)00144-6
  81. Stephanopoulos, G. N., A. A. Aristidou and J. Nielsen. 1998. Metabolic Engineering: Principles and Methodologies. Amsterdam, Nederland: Elsevier.
  82. Thiele, I. and B. O. Palsson. 2010. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protocols 5(1): 93-121. https://doi.org/10.1038/nprot.2009.203
  83. Varma, A. and B. O. Palsson. 1994a. Metabolic flux balancing: basic concepts, scientific and practical use. Nature Biotechnology 12(10): 994-998. https://doi.org/10.1038/nbt1094-994
  84. Varma, A. and B. O. Palsson. 1994b. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Applied and Environmental Microbiology 60(10): 3724-3731.
  85. Villeger, A. C., S. R. Pettifer and D. B. Kell. 2010. Arcadia: a visualization tool for metabolic pathways. Bioinformatics 26(11): 1470-1471. https://doi.org/10.1093/bioinformatics/btq154
  86. Voit, E. O. 2012. A First Course in Systems Biology. New York, USA: Garland Science.
  87. Voit, E. O. 2013. Biochemical systems theory: a review. ISRN Biomathematics 2013(6): 1-53.
  88. Waghorn, G. C. and R. L. Baldwin. 1984. Model of metabolite flux within mammary gland of the lactating cow. Journal of Dairy Science 67(3): 531-544. https://doi.org/10.3168/jds.S0022-0302(84)81336-3
  89. Wastney, M. E., B. H. Patterson, O. A. Linares, P. C. Greif and R. C. Boston. 1999. Investigating Biological Systems Using Modeling: Strategies and Software. MA, USA: Academic Press.
  90. Wiechert, W. 2002. Modeling and simulation: Tools for metabolic engineering. Journal of Biotechnology 94(1): 37-63. https://doi.org/10.1016/S0168-1656(01)00418-7
  91. Yim, H., R. Haselbeck, W. Niu, C. Pujol-Baxley, A. Burgard, J. Boldt, J. Khandurina, J. D. Trawick, R. E. Osterhout, R. Stephen, J. Estadilla, S. Teisan, H. B. Schreyer, S. Andrae, T. H. Yang, S. Y. Lee, M. J. Burk and S. Van Dien. 2011. Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nature Chemical Biology 7(7): 445-452. https://doi.org/10.1038/nchembio.580
  92. Zhao, H., M. Li, K. Fang, W. Chen and J. Wang. 2012. In silico insights into the symbiotic nitrogen fixation in sinorhizobium meliloti via metabolic reconstruction. PLoS ONE 7(2): e31287. https://doi.org/10.1371/journal.pone.0031287