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Integration of Metabolic Reactions and Gene Regulation

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Abstract

Metabolic reactions and gene regulation are two primary processes of cells. In response to environmental changes cells often adjust the regulatory programs and shift the metabolic states. An integrative investigation and modeling of these two processes would improve our understanding about the cellular systems and may generate substantial impacts in medicine, agriculture, environmental protection, and energy production. We review the studies of the various aspects of the crosstalk between metabolic reactions and gene regulation, including models, empirical evidence, and available databases.

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References

  1. Dyson, R. (1999). Origins of life. New York: Cambridge University Press.

    Book  Google Scholar 

  2. Maynard Smith, J., & Szathmary, E. (1999). The origins of life: From the birth of life to the origin of language. New York: Oxford University Press.

    Google Scholar 

  3. Varma, A., & Palsson, B. O. (1994). 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.

    CAS  Google Scholar 

  4. Bonarius, H. P. J., Schmid, G., & Tramper, J. (1997). Flux analysis of underdetermined metabolic networks: The quest for the missing constraints. Trends in Biotechnology, 15, 308–314.

    Article  CAS  Google Scholar 

  5. Edwards, J. S., & Palsson, B. O. (1998). How will bioinformatics influence metabolic engineering? Biotechnology and Bioengineering, 58, 162–169.

    Article  CAS  Google Scholar 

  6. Varma, A., & Palsson, B. O. (1993). Metabolic capabilities of Escherichia coli. II. Optimal growth patterns. Journal of Theoretical Biology, 165, 503–522.

    Article  CAS  Google Scholar 

  7. Danzig, G. B., Orden, A., & Wolfe, P. (2003). The generalized simplex method for minimizing a linear form under linear inequality restraints. In R. W. Cottle (Ed.), The basic George B. Danzig. Stanford: Stanford University Press.

  8. Karmarkar, N. (1984). A new polynomial-time algorithm for linear programming. Combinatorica, 4(4), 373–395.

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Schuster, S., Dandekar, T., & Fell, D. A. (1999). Detection of elementary flux modes in biochemical networks: A promising tool for pathway analysis and metabolic engineering. Trends in Biotechnology, 17, 53–60.

    Article  CAS  Google Scholar 

  11. Schuster, S., Fell, D. A., & Dandekar, T. A. (2000). A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nature Biotechnology, 18, 326–332.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  13. Wiechert, W. (2001). C13 metabolic flux analysis. Metabolic Engineering, 3, 195–206.

    Article  CAS  Google Scholar 

  14. Emmerling, M., Dauner, M., Ponti, A., Fiaux, J., Hochuli, M., Szyperski, T., et al. (2002). Metabolic flux responses to pyruvate kinase knockout in Escherichia coli. Journal of Bacteriology, 184(1), 152–164.

    Article  CAS  Google Scholar 

  15. Hua, Q., Yang, C., Baba, T., Mori, H., & Shimizu, K. (2003). Response of the central metabolism in Escherichia coli to phosphoglucose isomerase and glucose-6-phosphate dehydrogenase knockouts. Journal of Bacteriology, 185(24), 7053–7067.

    Article  CAS  Google Scholar 

  16. Fischer, E., Zamboni, N., & Sauer, U. (2004). High-throughput metabolic flux analysis based on gas chromatography-mass spectrometry derived C13 constraints. Analy. Biochem., 325, 308–316.

    Article  CAS  Google Scholar 

  17. Velagapudi, V. R., Wittmann, C., Schneider, K., & Heinzle, E. (2007). Metabolic flux screening of Saccharomyces cerevisiae single knockout strains on glucose and galactose supports elucidation of gene function. Journal of Bacteriology, 132(4), 395–404.

    CAS  Google Scholar 

  18. Costenoble, R., Muller, D., Barl, T., van Gulik, W. M., van Winden, W. A., Reuss, M., et al. (2007). 13C-Labeled metabolic flux analysis of a fed-batch culture of elutriated Saccharomyces cerevisiae. FEMS Yeast Research, 7(4), 511–526.

    Article  CAS  Google Scholar 

  19. Kleijn, R. J., Geertman, J. M., Nfor, B. K., Ras, C., Schipper, D., Pronk, J. T., et al. (2007). Metabolic flux analysis of a glycerol-overproducing Saccharomyces cerevisiae strain based on GC-MS, LC-MS and NMR-derived C-labelling data. FEMS Yeast Research, 7(2), 216–231.

    Article  CAS  Google Scholar 

  20. Covert, M., Schilling, C., & Palsson, B. O. (2001). Regulation of gene expression in flux balance models of metabolism. Journal of Theoretical Biology, 213, 73–78.

    Article  CAS  Google Scholar 

  21. Covert, M., & Palsson, B. O. (2003). Constraints-based models: Regulation of gene expression reduces the steady-state solution space. Journal of Theoretical Biology, 221, 309–325.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  23. Kacser, H., & Burns, J. A. (1973). The control of flux. Symposia of the Society for Experimental Biology, 27, 65–104.

    CAS  Google Scholar 

  24. Lehninger, A. L. (1982). Principles of biochemistry. New York: Worth Publishers.

    Google Scholar 

  25. Segre, D., Vitkup, D., & Church, G. (2002). Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences of the United States of America, 99(23), 15112–15117.

    Article  CAS  Google Scholar 

  26. Segre, D., Zucker, J., Katz, J., Lin, X., D’haeseleer, P., et al. (2003). From annotated genomes to metabolic flux models and kinetic parameter fitting. OMICS, 7(3), 301–316.

    Article  CAS  Google Scholar 

  27. Bertsekas, D. (1995). Nonlinear programming. Belmont, MA: Athena Scientific.

    Google Scholar 

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

    Article  CAS  Google Scholar 

  29. Uyeda, L. (1979). Phosphofructokinase. Advances in Enzymology and Related Ares of Molecular Biology, 48, 193–244.

    CAS  Google Scholar 

  30. Waygood, E. B., Mort, J. S., & Sanwal, B. D. (1976). The control of pyruvate kinase of Escherichia coli. Binding of substrate and allosteric effectors to the enzyme activated by fructose 1,6-bisphosphate. Biochemistry, 15(2), 277–282.

    Article  CAS  Google Scholar 

  31. Stolovicki, E., Dror, T., Brenner, N., & Braun, E. (2006). Synthetic gene recruitment reveals adaptive reprogramming of gene regulation in yeast. Genetics, 173(1), 75–85.

    Article  CAS  Google Scholar 

  32. Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabasi, A. L. (2000). The large-scale organization of metabolic networks. Nature, 407(6804), 651–654.

    Article  CAS  Google Scholar 

  33. Carlson, J. M., & Doyle, J. (2002). Complexity and robustness. Proceedings of the National Academy of Science, 99(Suppl 1), 2538–2545.

    Article  Google Scholar 

  34. Iuchi, S. (1993). Phosphorylation/dephosphorylation of the receiver module at the conserved aspartate residue controls transphosphorylation activity of histidine kinase in sensor protein ArcB of Escherichia coli. Journal of Biological Chemistry, 268(32), 23972–23980.

    CAS  Google Scholar 

  35. Iuchi, S., & Lin, E. C. (1988). ArcA (dye), a global regulatory gene in Escherichia coli mediating repression of enzymes in aerobic pathways. Proceedings of the National Academy of Science, 85(6), 1888–1892.

    Article  CAS  Google Scholar 

  36. Lynch, A. S., & Lin, E. C. (1996). Transcriptional control mediated by the ArcA two-component response regulator protein of Escherichia coli: Characterization of DNA binding at target promoters. Journal of Bacteriology, 178(21), 6238–6249.

    CAS  Google Scholar 

  37. Park, S. J., McCabe, J., Turna, J., & Gunsalus, R. P. (1994). Regulation of the citrate synthase (gltA) gene of Escherichia coli in response to anaerobiosis and carbon supply: Role of the arcA gene product. Journal of Bacteriology, 176(16), 5086–5092.

    CAS  Google Scholar 

  38. Park, S. J., Cotter, P. A., & Gunsalus, R. P. (1995). Regulation of malate dehydrogenase (mdh) gene expression in Escherichia coli in response to oxygen, carbon, and heme availability. Journal of Bacteriology, 177(22), 6652–6656.

    CAS  Google Scholar 

  39. Park, S. J., Tseng, C. P., & Gunsalus, R. P. (1995). Regulation of succinate dehydrogenase (sdhCDAB) operon expression in Escherichia coli in response to carbon supply and anaerobiosis: Role of ArcA and Fnr. Molecular Microbiology, 15(3), 473–482.

    Article  CAS  Google Scholar 

  40. Park, S. J., Chao, G., & Gunsalus, R. P. (1997). Aerobic regulation of the sucABCD genes of Escherichia coli, which encode alpha-ketoglutarate dehydrogenase and succinyl coenzyme A synthetase: Roles of ArcA, Fnr, and the upstream sdhCDAB promoter. Journal of Bacteriology, 179(13), 4138–4142.

    CAS  Google Scholar 

  41. Perrenoud, A., & Sauer, U. (2005). Impact of global transcriptional regulation by ArcA, ArcB, Cra, Crp, Cya, Fnr, and Mlc on glucose catabolism in Escherichia coli. Journal of Bacteriology, 187(9), 3171–3179.

    Article  CAS  Google Scholar 

  42. Saier, M. H., Ramseier, T. M., & Reizer, J. (1996). Regulation of carbon utilization. In F. C. Neidhardt, et al. (Eds.), Escherichia coli and Salmonella: Cellular and molecular biology. Washington, DC: ASM Press.

    Google Scholar 

  43. Zheng, D., Constantinidou, C., Hobman, J. L., & Minchin, S. D. (2004). Identification of the CRP regulon using in vitro and in vivo transcriptional profiling. Nucleic Acids Research, 32(19), 5874–5893.

    Article  CAS  Google Scholar 

  44. Grainger, D. C., Hurd, D., Harrison, M., Holdstock, J., & Busby, S. J. (2005). Studies of the distribution of Escherichia coli cAMP-receptor protein and RNA polymerase along the E. coli chromosome. Proceedings of the National Academy of Sciences of the United States of America, 102(49), 17693–17698.

    Article  CAS  Google Scholar 

  45. Makman, R. S., & Sutherland, E. W. (1965). Adenosine 3′, 5′-phosphate in Escherichia coli. Journal of Biological Chemistry, 240, 1309–1314.

    CAS  Google Scholar 

  46. Saier, M. H., & Ramseier, T. M. (1997). The catabolite repressor/activator (Cra) protein of enteric bacteria. Journal of Bacteriology, 178, 3411–3417.

    Google Scholar 

  47. Henikoff, S., Haughn, G. W., Calvo, J. M., & Wallace, J. C. (1988). A large family of bacterial activator proteins. Proceedings of the National Academy of Sciences of the United States of America, 85(18), 6602–6606.

    Article  CAS  Google Scholar 

  48. Su, C. H., & Greene, R. C. (1971). Regulation of methionine biosynthesis in Escherichia coli: Mapping of the metJ locus and properties of a metJ plus-metJ minus diploid. Proceedings of the National Academy of Sciences of the United States of America, 68(2), 367–371.

    Article  CAS  Google Scholar 

  49. Pittard, J., Camakaris, H., & Yang, J. (2005). The TyrR regulon. Molecular Microbiology, 55(1), 16–26.

    Article  CAS  Google Scholar 

  50. Griggs, D., & Johnston, M. (1991). Regulated expression of Gal4 activator gene in yeast provides a sensitive genetic switch for glucose repression. Proceedings of the National Academy of Sciences of the United States of America, 88(19), 8597–8601.

    Article  CAS  Google Scholar 

  51. Lohr, D., Venkov, P., & Zlatanova, J. (1995). Transcriptional regulation in the yeast Gal gene family: A complex genetic network. The FASEB Journal, 9, 777–787.

    CAS  Google Scholar 

  52. Natarajan, K., Meyer, M. R., Jackson, B. M., Slade, D., Roberts, C., Hinnebusch, A. G., et al. (2001). Transcriptional profiling shows that Gcn4p is a master regulator of gene expression during amino acid starvation in yeast. Molecular and Cellular Biology, 21(13), 4347–4368.

    Article  CAS  Google Scholar 

  53. Lee, T., Rinaldi, N., Robert, F., Odom, D., Bar-Joseph, Z., et al. (2002). A transcriptional regulatory network map for Saccharomyces cerevisiae. Science, 298, 799–804.

    Article  CAS  Google Scholar 

  54. Denis, V., Boucherie, H., Monribot, C., & Daignan-Fornier, B. (1998). Role of the Myb-like protein Bas1p in Saccharomyces cerevisiae: A proteome analysis. Molecular Microbiology, 30(3), 557–566.

    Article  CAS  Google Scholar 

  55. Xiao, W., & Rank, G. (1990). Branched chain amino acid regulation of the ilv2 locus in Saccharomyces cerevisiae. Genome, 33(4), 596–603.

    CAS  Google Scholar 

  56. O’Connel, K., Surdin-Kerjan, Y., & Baker, R. (1995). Role of the Saccharomyces cerevisiae general regulatory factor cp1 in methionine biosynthetic gene transcription. Molecular and Cellular Biology, 15, 1879–1888.

    Google Scholar 

  57. Carroll, S. B. (2005). Evolution at two levels: On genes and form. PLoS Biol, 3(7), e245.

    Article  CAS  Google Scholar 

  58. Barrangou, R., Azcarate-Peril, M. A., Duong, T., Conners, S., Kelly, R. M., & Klaenhammer, T. R. (2006). Global analysis of carbohydrate utilization by Lactobacillus acidophilus using cDNA microarrays. Proceedings of the National Academy of Sciences of the United States of America, 103(10), 3816–3821.

    Article  CAS  Google Scholar 

  59. Hua, Q., Yang, C., Baba, T., Mori, H., & Shimizu, K. (2004). Analysis of gene expression in Escherichia coli in response to changes of growth-limiting nutrient in chemostat cultures. Applied and Environmental Microbiology, 70(4), 2354–2366.

    Article  CAS  Google Scholar 

  60. Oh, M. K., & Liao, J. (2000). Gene expression profiling by DNA microarrays and metabolic fluxes in Escherichia coli. Biotechnology Progress, 16, 278–286.

    Article  CAS  Google Scholar 

  61. Monod, J. D. (1947). The phenomenon of enzymatic adaptation and its bearing on problems of genetics and cellular differentiation. Growth, 11, 223–289.

    CAS  Google Scholar 

  62. Jacob, F., & Monod, J. (1961). Genetic regulatory mechanisms in the synthesis of proteins. Journal of Molecular Biology, 3, 318–356.

    Article  CAS  Google Scholar 

  63. Smeianov, V. V., Wechter, P., Broadbent, J. R., Hughes, J. E., Rodriguez, B. T., et al. (2007). Comparative high-density microarray analysis of gene expression during growth of Lactobacillus helveticus in milk versus rich culture medium. Applied and Environmental Microbiology, 73(8), 2661–2672.

    Article  CAS  Google Scholar 

  64. Durfee, T., Hansen, A. M., Zhi, H., Blattner, F. R., & Jin, D. J. (2008). Transcription profiling of the stringent response in Escherichia coli. Journal of Bacteriology, 190(3), 1084–1096.

    Article  CAS  Google Scholar 

  65. Gutierrez, R. A., Lejay, L. V., Dean, A., Chiaromonte, F., Shasha, D. E., & Coruzzi, G. M. (2007). Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis. Genome Biology, 8(1), R7.

    Article  CAS  Google Scholar 

  66. Ma, S., Gong, Q., & Bohnert, H. J. (2006). Dissecting salt stress pathways. Journal of Experimental Botany, 57(5), 1097–1107.

    Article  CAS  Google Scholar 

  67. Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., et al. (2000). Genomic expression programs in the response of yeast cells to environmental changes. Molecular Biology of the Cell, 11(12), 4241–4257.

    CAS  Google Scholar 

  68. Siddiquee, K. A., Arauzo-Bravo, M. J., & Shimizu, K. (2004). Effect of a pyruvate kinase (pykF-gene) knockout mutation on the control of gene expression and metabolic fluxes in Escherichia coli. FEMS Microbiology Letters, 235(1), 25–33.

    Article  CAS  Google Scholar 

  69. Ideker, T., Thorsson, V., Ranish, J. A., Christmas, R., Buhler, J., et al. (2001). Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science, 292(5518), 929–934.

    Article  CAS  Google Scholar 

  70. Hughes, T. R., Marton, M. J., Jones, A. R., Roberts, C. J., Stoughton, R., et al. (2000). Functional discovery via a compendium of expression profiles. Cell, 102(1), 109–126.

    Article  CAS  Google Scholar 

  71. Giaever, G., Chu, A. M., Ni, L., Connelly, C., Riles, L., et al. (2002). Functional profiling of the Saccharomyces cerevisiae genome. Nature, 418(6896), 387–391.

    Article  CAS  Google Scholar 

  72. Santangelo, G. M. (2006). Glucose signaling in Saccharomyces cerevisiae. Microbiology and Molecular Biology Reviews, 70(1), 253–282.

    Article  CAS  Google Scholar 

  73. Edwards, T. E., Klein, D. J., & Ferre-D’Amare, A. R. (2007). Riboswitches: Small-molecule recognition by gene regulatory RNAs. Current Opinion in Structural Biology, 17(3), 273–279.

    Article  CAS  Google Scholar 

  74. Winkler, W., Nahvi, A., & Breaker, R. R. (2002). Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression. Nature, 419(6910), 952–956.

    Article  CAS  Google Scholar 

  75. Nahvi, A., Sudarsan, N., Ebert, M., Zou, X., Brown, K. L., & Breaker, R. (2002). Genetic control by a metabolite binding mRNA. Chemistry and Biology, 9(9), 1043–1049.

    Article  CAS  Google Scholar 

  76. Kim, J. N., & Breaker, R. R. (2008). Purine sensing by riboswitches. Biology of the Cell, 100(1), 1–11.

    Article  CAS  Google Scholar 

  77. Harbison, C. T., Gordon, D. B., Lee, T. I., et al. (2004). Transcriptional regulatory code of a eukaryote genome. Nature, 431(7004), 99–104.

    Article  CAS  Google Scholar 

  78. Barski, A., Cuddapah, S., Cui, K., Roh, T. Y., Schones, D., Wang, Z., et al. (2007). High-resolution profiling of histone methylation in the human genome. Cell, 129, 823–837.

    Article  CAS  Google Scholar 

  79. Keene, J. D., & Tenenbaum, S. A. (2002). Eukaryotic mRNPs may represent posttranscriptional operons. Molecular Cell, 9(6), 1161–1167.

    Article  CAS  Google Scholar 

  80. Kharchenko, P., Church, G. M., & Vitkup, D. (2005). Expression dynamics of a cellular metabolic network. Molecular Systems Biology, 1, 2005.0016.

    Article  CAS  Google Scholar 

  81. Wei, H., Persson, S., Metha, T., Srinivasasainagendra, V., Chen, L., Page, G. P., et al. (2006). Transcriptional coordination of the metabolic network in Aabidopsis. Plant Physiology, 142, 762–774.

    Article  CAS  Google Scholar 

  82. Ge, H., Liu, Z., Church, G. M., & Vidal, M. (2001). Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nature Genetics, 29(4), 482–486.

    Article  CAS  Google Scholar 

  83. Zaslaver, A., Mayo, A. E., Rosenberg, R., Bashkin, P., Sberro, H., Tsalyuk, M., et al. (2004). Just-in-time transcription program in metabolic pathways. Nature Genetics, 36(5), 486–491.

    Article  CAS  Google Scholar 

  84. Ihmels, J., Levy, R., & Barkai, N. (2004). Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae. Nature Biotechnology, 22(1), 86–92.

    Article  CAS  Google Scholar 

  85. Bilu, Y., Shlomi, T., Barkai, N., & Ruppin, E. (2006). Conservation of expression and sequence of metabolic genes is reflected by activity across metabolic states. PLoS Computational Biology, 2(8), e106.

    Article  CAS  Google Scholar 

  86. Martinez-Perez, O., Lopez-Sanchez, A., Reyes-Ramirez, F., Floriano, B., & Santero, E. (2007). Integrated response to inducers by communication between a catabolic pathway and its regulatory system. Journal of Bacteriology, 189(10), 3768–3775.

    Article  CAS  Google Scholar 

  87. Karp, P. D., Ouzounis, C. A., Moore-Kochlacs, C., Goldovsky, L., Kaipa, P., et al. (2005). Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Research, 33(19), 6083–6089.

    Article  CAS  Google Scholar 

  88. Caspi, R., Foerster, H., Fulcher, C. A., Hopkinson, R., Ingraham, J., et al. (2006). MetaCyc: A multiorganism database of metabolic pathways and enzymes. Nucleic Acids Research, 34, D511–D516.

    Article  CAS  Google Scholar 

  89. Karp, P. D., Keseler, I. M., Shearer, A., Latendresse, M., Krummenacker, M., et al. (2007). Multidimensional annotation of the Escherichia coli K-12 genome. Nucleic Acids Research, 35(22), 7577–7590.

    Article  CAS  Google Scholar 

  90. Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., et al. (2007). Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proceedings of the National Academy of Science, 104(6), 1777–1782.

    Article  CAS  Google Scholar 

  91. Mueller, L. A., Zhang, P., & Rhee, S. Y. (2003). AraCyc: A biochemical pathway database for Arabidopsis. Plant Physiology, 132(2), 453–460.

    Article  CAS  Google Scholar 

  92. Cherry, J. M., Ball, C., Weng, S., Juvik, G., Schmidt, R., et al. (1997). Genetic and physical maps of Saccharomyces cerevisiae. Nature, 387(6632 Suppl), 67–73.

    CAS  Google Scholar 

  93. Mewes, H. W., Frishman, D., Gruber, C., Geier, B., Haase, D., et al. (2000). MIPS: A database for genomes and protein sequences. Nucleic Acids Research, 28(1), 37–40.

    Article  CAS  Google Scholar 

  94. Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 28, 27–30.

    Article  CAS  Google Scholar 

  95. Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J., & Palsson, B. O. (2004). Integrating high-throughput and computational data elucidates bacterial networks. Nature, 429(6987), 92–96.

    Article  CAS  Google Scholar 

  96. Herrgard, M. J., Fong, S. S., & Palsson, B. O. (2006). Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Computational Biology, 2(7), e72.

    Article  CAS  Google Scholar 

  97. Shlomi, T., Eisenberg, Y., Sharan, R., & Ruppin, E. (2007). A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Molecular Systems Biology, 3, 101.

    Article  Google Scholar 

  98. Gat-Viks, I., Tanay, A., & Shamir, R. (2004). Modeling and analysis of heterogeneous regulation in biological networks. Journal of Computational Biology, 11(6), 1034–1049.

    Article  CAS  Google Scholar 

  99. Yeang, C. H., & Vingron, M. (2006). A joint model of regulatory and metabolic networks. BMC Bioinformatics, 7, 332.

    Article  CAS  Google Scholar 

  100. Narang, A. (2006). Comparative analysis of some models of gene regulation in mixed-substrate microbial growth. Journal of Theoretical Biology, 242(2), 489–501.

    Article  CAS  Google Scholar 

  101. Kofahl, B., & Klipp, E. (2004). Modelling the dynamics of the yeast pheromone pathway. Yeast, 21(10), 831–850.

    Article  CAS  Google Scholar 

  102. Varner, J. D. (2000). Large-scale prediction of phenotype: Concept. Biotechnology and Bioengineering, 69(6), 664–678.

    Article  CAS  Google Scholar 

  103. Burgard, A. P., Pharkya, P., & Maranas, C. D. (2003). Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering, 84(6), 647–657.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  105. Kim, H. U., Kim, T. Y., & Lee, S. Y. (2008). Metabolic flux analysis and metabolic engineering of microorganisms. Molecular BioSystems, 4, 113–120.

    Article  CAS  Google Scholar 

  106. Hirai, M. Y., Yano, M., Yano, M., Goodenowe, D. B., Kanaya, S., Kimura, T., et al. (2004). Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proceedings of the National Academy of Sciences of the United States of America, 101(27), 10205–10210.

    Article  CAS  Google Scholar 

  107. Barros, E., Lezar, S., Anttonen, M. J., van Dijk, J. P., Roehlig, R. M., Kok, E. J., et al. (2010). Comparison of two GM maize varieties with a near-isogenic non-GM variety using transcriptomics, proteomics and metabolomics. Plant Biotechnology Journal, 8, 436–451.

    Article  CAS  Google Scholar 

  108. Skirycz, A., De Bodt, S., Obata, T., De Clercq, I., Claeys, H., De Rycke, R., et al. (2010). Developmental stage specificity and the role of mitochondrial metabolism in the response of Arabidopsis leaves to prolonged mild osmotic stress. Plant Physiology, 152, 226–244.

    Article  CAS  Google Scholar 

  109. Depuydt, S., Trenkamp, S., Fernie, A. R., Elftieh, S., Renou, J. P., Vuylsteke, M., et al. (2009). An Integrated genomics approach to define niche establishment by Rhodococcus fascians. Plant Physiology, 149, 1366–1386.

    Article  CAS  Google Scholar 

  110. Malitsky, S., Blum, E., Less, H., Venger, I., Elbaz, M., Morin, S., et al. (2008). The transcript and metabolite networks affected by the two clades of Arabidopsis glucosinolate biosynthesis regulators. Plant Physiology, 148, 2021–2049.

    Article  CAS  Google Scholar 

  111. Farag, M. A., Deavours, B. E., De Fatima, A., Naoumkina, M., Dixon, R. A., & Sumner, L. W. (2009). Integrated metabolite and transcript profiling identify a biosynthetic mechanism for hispidol in Medicago truncatula cell cultures. Plant Physiology, 151, 1096–1113.

    Article  CAS  Google Scholar 

  112. Urano, K., Kurihara, Y., Seki, M., & Shinozaki, K. (2010). ‘Omics’ analyses of regulatory networks in plant abiotic stress responses. Current Opinion in Plant Biology, 13, 132–138.

    Article  CAS  Google Scholar 

  113. Iyer-Pascuzzi, A., Simpson, J., Herrera-Estrella, L., & Benfey, P. N. (2009). Functional genomics of root growth and development in Arabidopsis. Current Opinion in Plant Biology, 12(2), 165–171.

    Article  CAS  Google Scholar 

  114. Lee, S. J., Trostela, A., Lea, P., Harinarayananb, R., FitzGerald, P. C., & Adhya, S. (2009). Cellular stress created by intermediary metabolite imbalances. Proceedings of the National Academy of Sciences of the United States of America, 106(46), 19515–19520.

    Article  CAS  Google Scholar 

  115. Rossouw, D., Naes, T., & Bauer, F. F. (2008). Linking gene regulation and the exo-metabolome: A comparative transcriptomics approach to identify genes that impact on the production of volatile aroma compounds in yeast. BMC Genomics, 9, 530.

    Article  CAS  Google Scholar 

  116. Tan, K. C., Ipcho, S. V. S., Trengove, R. D., Olivier, R. P., & Solomon, P. S. (2009). Assessing the impact of transcriptomics, proteomics and metabolomics on fungal phytopathology. Molecular Plant Pathology, 10(5), 703–715.

    Article  CAS  Google Scholar 

  117. Andersen, M. R., & Nielsen, J. (2009). Current status of systems biology in Aspergilli. Fungal Genetics and Biology, 46, S180–S190.

    Article  CAS  Google Scholar 

  118. Park, S. J., Lee, S. Y., Cho, J., Kim, T. Y., Lee, J. W., Park, J. H., et al. (2005). Global physiological understanding and metabolic engineering of microorganisms based on omics studies. Applied Microbiology and Biotechnology, 68, 567–579.

    Article  CAS  Google Scholar 

  119. Raes, J., & Bork, P. (2008). Molecular eco-systems biology: Towards an understanding of community function. Nature Reviews Microbiology, 6, 693–699.

    Article  CAS  Google Scholar 

  120. Yang, X., Zhang, B., Molony, C., Chudin, E., Hao, K., Zhu, J., et al. (2010). Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver. Genome Research, 20(8), 1020–1036.

    Article  CAS  Google Scholar 

  121. Waterman, C. L., Currie, R. A., Cottrell, L. A., Dow, J., Wright, J., Waterfield, C. J., et al. (2010). An integrated functional genomic study of acute phenobarbital exposure in the rat. BMC Genomics, 11, 9.

    Article  CAS  Google Scholar 

  122. Bundy, J. G., Sidhu, J. K., Rana, F., Spurgeon, D. J., Svendsen, C., Wren, J. F., et al. (2008). ‘Systems toxicology’ approach identifies coordinated metabolic responses to copper in a terrestrial non-model invertebrate, the earthworm Lumbricus rubellus. BMC Biology, 6, 25.

    Article  CAS  Google Scholar 

  123. Kaddurah-Daouk, R., & Krishnan, K. R. R. (2009). Metabolomics: A global biochemical approach to the study of central nervous system diseases. Neuropsychopharmacology Reviews, 34, 173–186.

    Article  CAS  Google Scholar 

  124. Wang, J., Wu, G., Zhou, H., & Wang, F. (2009). Emerging technologies for amino acid nutrition research in the post-genome era. Amino Acids, 37, 177–186.

    Article  CAS  Google Scholar 

  125. Tringe, S. G., & Rubin, E. M. (2005). Metagenomics: DNA sequencing of environmental samples. Nature Reviews Genetics, 6(11), 805–814.

    Article  CAS  Google Scholar 

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Correspondence to Chen-Hsiang Yeang.

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Yeang, CH. Integration of Metabolic Reactions and Gene Regulation. Mol Biotechnol 47, 70–82 (2011). https://doi.org/10.1007/s12033-010-9325-y

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