Skip to main content
Download PDF

On the way to understand biological complexity in plants: S-nutrition as a case study for systems biology

Cellular & Molecular Biology Letters volume 11, pages 37–56 (2006)Cite this article

Abstract

The establishment of technologies for high-throughput DNA sequencing (genomics), gene expression (transcriptomics), metabolite and ion analysis (metabolomics/ionomics) and protein analysis (proteomics) carries with it the challenge of processing and interpreting the accumulating data sets. Publicly accessible databases and newly development and adapted bioinformatic tools are employed to mine this data in order to filter relevant correlations and create models describing physiological states. These data allow the reconstruction of networks of interactions of the various cellular components as enzyme activities and complexes, gene expression, metabolite pools or pathway flux modes. Especially when merging information from transcriptomics, metabolomics and proteomics into consistent models, it will be possible to describe and predict the behaviour of biological systems, for example with respect to endogenous or environmental changes. However, to capture the interactions of network elements requires measurements under a variety of conditions to generate or refine existing models. The ultimate goal of systems biology is to understand the molecular principles governing plant responses and consistently explain plant physiology.

References

  1. Schuster, S., Fell, D.A. and Dandekar, T. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat. Biotechnol. 18 (2000) 267–268.

    Article  Google Scholar 

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

    PubMed  CAS  Article  Google Scholar 

  3. Papin, J.A., Price, N.D., Wiback, S.J., Fell, D.A. and Palsson, B.O. Metabolic pathways in the post-genome era. Trends Biochem. Sci. 28 (2003) 250–258.

    PubMed  CAS  Article  Google Scholar 

  4. Holtorf, H., Guitton, M.-C. and Reski, R. Plant functional genomics. Naturwissenschaften 89 (2002) 235–249.

    PubMed  CAS  Article  Google Scholar 

  5. Oliver, S.G., Winson, M.K., Kell, D.B. and Baganz, F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 16 (1998) 373–378.

    PubMed  CAS  Article  Google Scholar 

  6. Blackstock, W.P. and Weir, M.P. Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol. 17 (1999) 121–127.

    PubMed  CAS  Article  Google Scholar 

  7. Thiellement, H., Bahrman, N., Damerval, C., Plomion, C., Rossingnol, M., Santoni, V., de Vienne, D. and Zivy, M. Proteomics for genetic and physiological studies in plants. Electrophoresis 20 (1999) 2013–2026.

    PubMed  CAS  Article  Google Scholar 

  8. van Wijk, K.J. Update on plant proteomics. Challenges and prospects of plant proteomics. Plant Physiol. 126 (2001) 501–508.

    PubMed  Article  Google Scholar 

  9. Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R.N. and Willmitzer, L. Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18 (2000) 1157–1161.

    PubMed  CAS  Article  Google Scholar 

  10. Trethewey, R.N., Krotzky, A.J. and Willmitzer, L. Metabolic profiling: a rosetta stone for genomics? Curr. Opin. Plant Biol. 2 (1999) 83–85.

    PubMed  CAS  Article  Google Scholar 

  11. Trethewey, R.N. Gene discovery via metabolic profiling. Curr. Opin. Biotechnol. 12 (2001) 135–138.

    PubMed  CAS  Article  Google Scholar 

  12. Trethewey, R.N. Metabolite profiling as an aid to metabolic engineering in plants. Curr. Opin. Plant Biol. 7 (2004) 196–201.

    PubMed  CAS  Article  Google Scholar 

  13. Harmer, S.L., Hogenesch, L.B., Straume, M., Chang, H.S., Han, B., Zhu, T., Wang, X., Kreps, J.A. and Kay, S.A. Orchestrated transcription of key pathways in Arabidopsis by the circadian clock. Science 290 (2000) 2110–2113.

    PubMed  CAS  Article  Google Scholar 

  14. Goda, H., Shimada, Y., Asami, T., Fujioka, S. and Yoshida, S. Microarray analysis of brassinosteroid-regulated genes in Arabidopsis. Plant Physiol. 130 (2002) 1319–1334.

    PubMed  CAS  Article  Google Scholar 

  15. Müssig, C., Fischer, S. and Altmann, T. Brassinosteroid-regulated gene expression. Plant Physiol. 129 (2002) 1241–1251.

    PubMed  Article  Google Scholar 

  16. Rashotte, A.M., Carson, S.D., To, J.P. and Kieber, J.J. Expression profiling of cytokinin action in Arabidopsis. Plant Physiol. 132 (2003) 1998–2011.

    PubMed  CAS  Article  Google Scholar 

  17. Kreps, J.A., Wu, Y., Chang, H.S., Zhu, T., Wang, X. and Harper, J. Transcriptome changes for Arabidopsis in response to salt, osmotic, and cold stress. Plant Physiol. 130 (2002) 2129–2141.

    PubMed  CAS  Article  Google Scholar 

  18. Seki, M., Narusaka, M., Ishida, J., Nanjo, T., Fujita, M., Oono, Y., Kamiya, A., Nakajima, M., Enju, A., Sakurai, T., Satou, M., Akiyama, K., Taji, T., Yamaguchi-Shinozaki, K., Carninci, P., Kawai, J., Hayashizaki, Y. and Shinozaki, K. Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length cDNA microarray. Plant J. 31 (2002) 279–292.

    PubMed  CAS  Article  Google Scholar 

  19. Hammond J.P., Bennett, M.J., Bowen, H.C., Broadley, M.R., Eastwood, D.C., May, S.T., Rahn, C., Swarup, R., Woolaway, K.E. and White, P.J. Changes in gene expression in Arabidopsis shoots during phosphate starvation and the potential for developing smart plants. Plant Physiol. 132 (2003) 578–596.

    PubMed  CAS  Article  Google Scholar 

  20. Oono, Y., Seki, M., Nanjo, T., Narusaka, M., Fujita, M., Satoh, R., Satou, M., Sakurai, T., Ishida, J., Akiyama, K., Iida, K., Maruyama, K., Satoh, S., Yamaguchi-Shinozaki, K. and Shinozaki, K. Monitoring expression profiles of Arabidopsis gene expression during rehydration process after dehydration using ca. 7000 full-length cDNA microarray. Plant J. 34 (2003) 868–887.

    PubMed  CAS  Article  Google Scholar 

  21. Menges, M., Hennig, L., Gruissem, W. and Murray, J.A.H. Cell cycleregulated gene expression in Arabidopsis. J. Biol. Chem. 277 (2002) 41987–42002.

    PubMed  CAS  Article  Google Scholar 

  22. Tepperman, J.M., Zhu, T., Chang, H.S., Wang, X. and Quail, P.H. Multiple transcription-factor genes are early targets of phytochrome A signaling. Proc. Natl. Acad. Sci. USA 98 (2001) 9437–9442.

    PubMed  CAS  Article  Google Scholar 

  23. Che, P., Gingerich, D.J., Lall, S. and Howell, S.H. Global and hormone-induced gene expression changes during shoot development in Arabidopsis. Plant Cell 14 (2002) 2771–2785.

    PubMed  CAS  Article  Google Scholar 

  24. Honys, D. and Twell, D. Comparative analysis of the Arabidopsis pollen transcriptome. Plant Physiol. 132 (2003) 640–652.

    PubMed  CAS  Article  Google Scholar 

  25. Köhler, C., Hennig, L., Spillane, C., Pien, S., Gruissem, W. and Grossniklaus, U. The Polycomb-group protein MEDEA regulates seed development by controlling expression of the MADS-box gene PHERES1. Genes Dev. 17 (2003) 1540–1553.

    PubMed  Article  Google Scholar 

  26. Puthoff, D.P., Nettleton, D., Rodermel, S.R. and Baum, T.J. Arabidopsis gene expression changes during cyst nematode parasitism revealed by statistical analyses of microarray expression profiles. Plant J. 33 (2003) 911–921.

    PubMed  CAS  Article  Google Scholar 

  27. Tao, Y., Xie, Z., Chen, W., Glazebrook, J., Chang, H.S., Han, B., Zhu, T., Zou, G. and Katagiri, F. Quantitative nature of Arabidopsis responses during compatible and incompatible interactions with the bacterial pathogen Pseudomonas syringae. Plant Cell 15 (2003) 317–330.

    PubMed  CAS  Article  Google Scholar 

  28. Hampton, C.R., Bowen, H.C., Broadley, M.R., Hammond, J.P., Mead, A., Payne, K.A., Pritchard, J. and White, P.J. Cesium Toxicity in Arabidopsis. Plant Physiol. 136 (2004) 3824–3837.

    PubMed  CAS  Article  Google Scholar 

  29. Sahr, T., Voigt, G., Paretzke, H.G., Schramel, P. and Ernst, D. Caesium-affected gene expression in Arabidopsis thaliana. New Phytologist 165 (2005) 747–754.

    PubMed  CAS  Article  Google Scholar 

  30. Laule, O., Fürholz, A., Chang, H.S., Zhu, T., Wang, X., Heifetz, P.B., Gruissem, W. and Lange, M. Crosstalk between cytosolic and plastidial pathways of isoprenoid biosynthesis in Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA 100 (2003) 6866–6871.

    PubMed  CAS  Article  Google Scholar 

  31. Wang, R., Guegler, K., LaBrie, S.T. and Crawford, N.M. Genomic analysis of a nutrient response in Arabidopsis reveals diverse expression patterns and novel metabolic and potential regulatory genes induced by nitrate. Plant Cell 12 (2000) 1491–1509.

    PubMed  CAS  Article  Google Scholar 

  32. Wang, Y.-H., Garvin, D.F. and Kochian, L.V. Nitrate-induced genes in tomato roots. Array analysis reveals novel genes that may play a role in nitrogen nutrition. Plant Physiol. 127 (2001) 345–359.

    PubMed  CAS  Article  Google Scholar 

  33. Colebatch, G., Kloska, S., Trevaskis, B., Freund, S., Altmann, T. and Udvardi, M.K. Novel aspects of symbiotic nitrogen fixation uncovered by transcript profiling with cDNA arrays. Mol. Plant Microbe Interact. 15 (2002) 411–420.

    PubMed  CAS  Google Scholar 

  34. Wang, Y.-H., Garvin, D.F. and Kochian, L.V. Rapid induction of regulatory and transporter genes in response to phosphorus, potassium, and iron deficiencies in tomato roots. Evidence for cross talk and root/rhizosphere-mediated signals. Plant Physiol. 130 (2002) 1361–1370.

    PubMed  CAS  Article  Google Scholar 

  35. Thimm, O., Essigmann, B., Kloska, S., Altmann, T. and Buckhout, T.J. Response of Arabidopsis to iron deficiency stress as revealed by microarray analysis. Plant Physiol. 127 (2001) 1030–1043.

    PubMed  CAS  Article  Google Scholar 

  36. Negishi, T., Nakanishi, H., Yazaki, J., Kishimoto, N., Fujii, F., Shimbo, K., Yamamoto, K., Sakata, K., Sasaki, T., Kikuchi, S., Mori, S. and Nishizawa, N.K. cDNA microarray analysis of gene expression during Fe-deficiency stress in barley suggests that polar transport of vesicles is implicated in phytosiderophore secretion in Fe-deficient barley roots. Plant J. 30 (2002) 83–94.

    PubMed  CAS  Article  Google Scholar 

  37. Wang, R., Okamoto, M., Xing, X. and Crawford, N.M. Microarray analysis of the nitrate response in Arabidopsis roots and shoots reveals over 1,000 rapidly responding genes and new linkages to glucose, trehalose-6-phosphate, Iron, and sulfate metabolism. Plant Physiol. 132 (2003) 556–567.

    PubMed  CAS  Article  Google Scholar 

  38. Maathuis, F.J.M., Filatov, V., Herzyk, P., Krijger, G.C., Axelsen, K.B., Chen, S., Green, B.J., Li, Y., Madagan, K.L., Sánchez-Fernández, R., Forde, B.G., Palmgren, M.G., Rea, P.A., Williams, L.E., Sanders, D. and Amtmann, A. Transcriptome analysis of root transporters reveals participation of multiple gene families in the response to cation stress. Plant J. 35 (2003) 675–692.

    PubMed  CAS  Article  Google Scholar 

  39. Hirai, Y.M., Fujiwara, T., Awazuhara, M., Kimura, T., Masaaki, N. and Saito, K. Global expression profiling of sulphur-starved Arabidopsis by DNA macroarray reveals the role of O-acetyl-L-serine as a general regulator of gene expression in response to sulphur nutrition. Plant J. 33 (2003) 651–663.

    PubMed  CAS  Article  Google Scholar 

  40. Maruyama-Nakashita, A., Inoue, E., Watanabe-Takahashi, A., Yamaya, T. and Takahashi, H. Transcriptome profiling of sulphur-responsive genes in Arabidopsis reveals global effects of sulphur nutrition on multiple metabolic pathways. Plant Physiol. 132 (2003) 597–605.

    PubMed  CAS  Article  Google Scholar 

  41. Nikiforova, V., Freitag, J., Kempa, S., Adamik, M., Hesse, H. and Hoefgen, R. Transcriptome analysis of sulphur depletion in Arabidopsis thaliana: interlacing of biosynthetic pathways provides response specificity. Plant J. 33 (2003) 633–650.

    PubMed  CAS  Article  Google Scholar 

  42. Maathuis, F.J.M. and Amtmann, A. Transcriptional profiling of membrane transporters. in: Plant Nutritional Genomics, (Broadley, M. and White, PJ. Eds.), Plant Sciences Division, School of Biosciences, University of Nottingham, UK, 2005, 170–200.

    Google Scholar 

  43. Nikiforova, V., Gakière, B., Kempa, S., Adamik, M., Willmitzer, L., Hesse, H. and Hoefgen, R. Towards dissecting nutrient metabolism in plants: a systems biology case study on sulfur metabolism. J. Exp. Bot. 55 (2004) 1861–1870.

    PubMed  CAS  Article  Google Scholar 

  44. Fiehn, O. Metabolomics — the link between genotypes and phenotypes. Plant Mol. Biol. 48 (2002) 155–171.

    PubMed  CAS  Article  Google Scholar 

  45. Sumner, L.W., Mendes, P. and Dixon, R.A. Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62 (2003) 817–836.

    PubMed  CAS  Article  Google Scholar 

  46. Stitt, M. and Fernie, A.R. From measurements of metabolites to metabolomics: an ‘on the fly’ perspective illustrated by recent studies of carbon-nitrogen interactions. Curr. Opin. Biotechnol. 14 (2003) 136–144.

    PubMed  CAS  Article  Google Scholar 

  47. Nielsen J. and Oliver S. The next wave in metabolome analysis. Trends Biotechnol. 23 (2005) 544–546.

    PubMed  CAS  Article  Google Scholar 

  48. The Arabidopsis Genome Initiative. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408 (2000) 796–815.

  49. Bino, R.J., Hall, R.D., Fiehn, O., Kopka, J., Saito, K., Draper, J., Nikolau, B.J., Mendes, P., Roessner-Tunali, U., Beale, M.H., Trethewey, R.N., Lange, B.M., Wurtele, E.S. and Sumner, L.W. Potential of metabolomics as a functional genomics tool. Trends Plant Sci. 9 (2004) 418–425.

    PubMed  CAS  Article  Google Scholar 

  50. Jenkins, H., Hardy, N., Beckmann, M., Draper, J., Smith, A.R., Taylor, J., Fiehn, O., Goodacre, R., Bino, R.J., Hall, R., Kopka, J., Lane, G.A., Lange, B.M., Liu, J.R., Mendes, P., Nikolau, B.J., Oliver, S.G., Paton, N.W., Rhee, S., Roessner-Tunali, U., Saito, K., Smedsgaard, J., Sumner, L.W., Wang, T., Walsh, S., Wurtele, E.S. and Kell, D.B.A. Proposed framework for the description of plant metabolomics experiments and their results. Nature Biotechnol. 22 (2004) 1601–1606.

    CAS  Article  Google Scholar 

  51. Schauer, N., Steinhauser, D., Strelkov, S., Schomburg, D., Allison, G., Moritz, T., Lundgren, K., Roessner-Tunali, U., Forbes, M.G., Willmitzer, L., Fernie, A.R. and Kopka, J. GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett. 579 (2005) 1332–1337.

    PubMed  CAS  Article  Google Scholar 

  52. Roessner, U., Wagner, C., Kopka, J., Trethewey, R.N. and Willmitzer, L. Technical advance: simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J. 23 (2000) 131–142.

    PubMed  CAS  Article  Google Scholar 

  53. Roessner, U., Luedemann, A., Brust, D., Fiehn, O., Linke, T., Willmitzer, L. and Fernie, A.R. Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell 13 (2001) 11–29.

    PubMed  CAS  Article  Google Scholar 

  54. Wagner, C., Sefkow, M. and Kopka, J. Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles. Phytochemistry 62 (2003) 887–900.

    PubMed  CAS  Article  Google Scholar 

  55. Fiehn, O. and Weckwerth, W. Deciphering metabolic networks. Eur. J. Biochem. 270 (2003) 579–588.

    PubMed  CAS  Article  Google Scholar 

  56. Ward, J.L., Harris, C., Lewis, J. and Beale, M.H. Assessment of 1H-NMR spectroscopy and multivariate analysis as a technique for metabolite fingerprinting of Arabidopsis thaliana. Phytochemistry 62 (2003) 949–957.

    PubMed  CAS  Article  Google Scholar 

  57. Ott, K.-H., Aranibar, N., Singh, B. and Stockton, G. Metabolomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts. Phytochemistry 62 (2003) 971–985.

    PubMed  CAS  Article  Google Scholar 

  58. Defernez, M. and Colquhoun, I.J. Factors affecting the robustness of metabolite fingerprinting using 1H-NMR spectra. Phytochemistry 62 (2003) 1009–1017.

    PubMed  CAS  Article  Google Scholar 

  59. Le Gall, G., Colquhoun, I.J., Davis, A.L., Collins, G.J. and Verhoeyen, M.E. Metabolite profiling of tomato (Lycopersicon esculentum) using 1H-NMR spectroscopy as a tool to detect potential unintended effects following a genetic modification. J. Agric. Food Chem. 51 (2003) 2447–2456.

    PubMed  Article  Google Scholar 

  60. Soga, T., Ueno, Y., Naraoka, H., Ohashi, Y., Tomita, M. and Nishioka, T. Simultaneous determination of anionic intermediates for Bacillus subtilis metabolic pathways by capillary electrophoresis electrospray ionization mass spectrometry. Anal. Chem. 74 (2002) 2233–2239.

    PubMed  CAS  Article  Google Scholar 

  61. Tolstikov, V.V. and Fiehn, O. Analysis of highly polar compounds of plant origin: combination of hydrophilic interaction chromatography and electrospray ion trap mass spectrometry. Anal. Biochem. 301 (2002) 298–307.

    PubMed  CAS  Article  Google Scholar 

  62. Rea, P.A. Ion genomics. Nature Biotechnol. 21 (2003) 1149–1151.

    CAS  Article  Google Scholar 

  63. Lahner, B., Gong, J., Mahmoudian, M., Smith, E.L., Abid, K.B., Rogers, E.E., Guerinot, M.L., Harper, J.F., Ward, J.M., McIntyre, L., Schroeder, J.I. and Salt, D.E. Genomic scale profiling of nutrient and trace elements in Arabidopsis thaliana. Nature Biotechnol. 21 (2003) 1215–1221.

    CAS  Article  Google Scholar 

  64. Salt, D.E. Update on Plant Ionomics. Plant Physiol. 136 (2004) 2451–2456.

    PubMed  CAS  Article  Google Scholar 

  65. Lahner, B. and Salt, D.E. Mapping links between the genome and ionome in plants. in: Plant Nutritional Genomics, (Broadley, M. and White, P.J. Eds.), Plant Sciences Division, School of Biosciences, University of Nottingham, UK, 2005, 150–170.

    Google Scholar 

  66. Nikiforova, V.J., Kopka, J., Tolstikov, V., Fiehn, O., Hopkins, L., Hawkesford, M.J., Hesse, H. and Hoefgen, R. Systems rebalancing of metabolism in response to sulfur deprivation, as revealed by metabolome analysis of Arabidopsis plants. Plant Physiol. 138 (2005) 304–318.

    PubMed  CAS  Article  Google Scholar 

  67. Molloy, M.P., Phadke, N.D., Maddock, J.R. and Andrews, P.C. Two-dimensional electrophoresis and peptide mass fingerprinting of bacterial outer membrane proteins. Electrophoresis 22 (2001) 1686–1696.

    PubMed  CAS  Article  Google Scholar 

  68. Lester, P. and Hubbard, S. Comparative bioinformatic analysis of complete protecomes and protein parameters for cross-species identification in proteomics. Proteomics 2 (2002) 1392–1405.

    PubMed  CAS  Article  Google Scholar 

  69. Görg, A., Obermaier, C., Boguth, G., Harder, A., Scheibe, B., Wildgruber, R. and Weiss W. The current state of two-dimensional electrophoresis with immobilized pH gradients. Electrophoresis 21 (2000) 1037–1053.

    PubMed  Article  Google Scholar 

  70. Wolters, D.A., Washburn, M.P. and Yates, J.R. 3rd. An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 73 (2001) 5683–5690.

    PubMed  CAS  Article  Google Scholar 

  71. Link, A.J., Eng, J., Schieltz, D.M., Carmack, E., Mize, G.J., Morris, D.R., Garvik, B.M. and Yates, J.R. 3rd. Direct analysis of protein complexes using mass spectrometry. Nature Biotechnol. 17 (1999) 676–682.

    CAS  Article  Google Scholar 

  72. Shen, Y. and Smith, R. Proteomics based on high-efficiency capillary separations. Electrophoresis 23 (2002) 3106–3124.

    PubMed  CAS  Article  Google Scholar 

  73. Jönsson, H., Shapiro, B.E., Meyerowitz, E.M. and Mjolsness, E. (2003) Signaling in multicellular models of plant development. In: On Growth, Form, and Computers (Kumar, S., Bentley, P.J., Eds.) Academic Press, London, UK, 156–161.

    Google Scholar 

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

    PubMed  CAS  Article  Google Scholar 

  75. Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Ziv, B.-J., Gerber, G.K., Hannett, N.M., Harbison, C., Thompson, C.M., Simon, I., Zeitlinger, J., Jennings, E.G., Murray, H.L., Gordon, D.B., Ren, B., Wyrick, J.J., Tagne, J.-B., Volkert, T.L., Fraenkel, E., Gifford, D.K. and Young, R.A. Transcriptional Regulatory Networks in Saccharomyces cerevisiae. Science 298 (2002) 799–804.

    PubMed  CAS  Article  Google Scholar 

  76. Buckhout, T.J. and Thimm, O. Insights into metabolism obtained from microarray analysis. Curr. Opin. Plant Biol. 6 (2003) 288–296.

    PubMed  CAS  Article  Google Scholar 

  77. Girke, T., Ozkan, M., Carter, D. and Raikhel, N.V. Towards a modeling infrastructure for studying plant cells. Plant Physiol. 132 (2003) 410–414.

    PubMed  CAS  Article  Google Scholar 

  78. Girke, T., Todd, J., Ruuska, S., White, J., Benning, C. and Ohlrogge, J. Microarray analysis of developing Arabidopsis seeds. Plant Physiol. 124 (2000) 1570–1581.

    PubMed  CAS  Article  Google Scholar 

  79. Jasny, B.R. and Ray, L.B. Life and the art of networks. Science 301 (2003) 1863.

    CAS  Article  Google Scholar 

  80. Bray, D. Molecular networks: The top-down view. Science 301 (2003) 1864–1865.

    PubMed  CAS  Article  Google Scholar 

  81. Alon, U. Biological networks: The tinkerer as an engineer. Science 301 (2003) 1866–1867.

    PubMed  CAS  Article  Google Scholar 

  82. Askenazi, M., Driggers, E.M., Holtzman, D.A., Norman, T.C., Iverson, S., Zimmer, D.P., Boers, M.E., Blomquist, P.R., Martinez, E.J., Monreal, A.W., Feibelman, T.P., Mayorga, M.E., Maxon, M.E., Sykes, K., Tobin, J.V., Cordero, E., Salama, S.R., Trueheart, J., Royer, J.C. and Madden, K.T. Integrating transcriptional and metabolite profiles to direct the engineering of lovastatin-producing fungal strains. Nature Biotechnol. 21 (2003) 150–156.

    CAS  Article  Google Scholar 

  83. Urbanczyk-Wochniak, E., Luedemann, A., Kopka, J., Selbig, J., Roessner-Tunali, U., Willmitzer, L. and Fernie, A.R. Parallel analysis of transcript and metabolic profiles: a new approach in systems biology. EMBO Rep. 4 (2003) 989–993.

    PubMed  CAS  Article  Google Scholar 

  84. Daub, C.O., Kloska, S. and Selbig, J. MetaGeneAlyse: analysis of integrated transcriptional and metabolite data. Bioinformatics 19 (2003) 2332–2333.

    PubMed  CAS  Article  Google Scholar 

  85. Thimm, O., Bläsing, O., Gibon, Y., Nagel, A., Meyer, S., Krüger, P., Müller, L.A., Rhee, S.Y. and Stitt, M. MapMan: A user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 37 (2004) 914–939.

    PubMed  CAS  Article  Google Scholar 

  86. Kitano, H. Systems biology: A brief overview. Science 295 (2002) 1662–1664.

    PubMed  CAS  Article  Google Scholar 

  87. Hirai, M.Y., Yano, M., Goodenowe, D.B., Kanaya, S., Kimura, T., Awazuhara, M., Arita, M., Fujiwara, T. and Saito, K. Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc. Nat. Acad. Sci. USA 101 (2004) 10205–10210.

    PubMed  CAS  Article  Google Scholar 

  88. Nikiforova, V.J., Daub, C.O., Hesse, H., Willmitzer, L. and Hoefgen, R. Integrative gene-metabolite network with implemented causality deciphers informational fluxes of sulphur stress response. J. Exp. Bot. 56 (2005) 1887–1896.

    PubMed  CAS  Article  Google Scholar 

  89. Hirai, M.Y., Klein, M., Fujikawa, Y., Yano, M., Goodenowe, D.B., Yamazaki, Y., Kanaya, S., Nakamura, Y., Kitayama, M., Suzuki, H., Sakurai, N., Shibata, D., Tokuhisa, J., Reichelt, M., Gershenzon, J., Papenbrock, J. and Saito K. Elucidation of gene-to-gene and metabolite-to-gene networks in Arabidopsis by integration of metabolomics and transcriptomics. J. Biol. Chem. 280 (2005) 25590–25595.

    PubMed  CAS  Article  Google Scholar 

  90. Gill, R.T., Katsoulakis, E., Schmitt, W., Taroncher-Oldenburg, G., Misra, J. and Stephanopoulos, G. Genome-wide dynamic transcriptional profiling of the light-to-dark transition in Synechocystis sp. strain PCC 6803. J. Bacteriol. 184 (2002) 3671–3681.

    PubMed  CAS  Article  Google Scholar 

  91. Graves, P. and Haystead, T. Molecular biologist’s guide to proteomics. Microbiol. Mol. Biol. Rev. 66 (2002) 39–63.

    PubMed  CAS  Article  Google Scholar 

  92. Katagiri, F. Attacking complex problems with the power of systems biology. Plant Physiol. 132 (2003) 417–419.

    PubMed  CAS  Article  Google Scholar 

  93. Sweetlove, L.J., Last, R.L. and Fernie, A.R. Predictive metabolic engineering: a goal for systems biology. Plant Physiol. 132 (2003) 420–442.

    PubMed  CAS  Article  Google Scholar 

  94. Minorsky, P.V. Achieving the in silico plant. Systems biology and the future of plant biological research. Plant Physiol. 132 (2003) 404–409.

    CAS  Article  Google Scholar 

  95. Chong, L. and Ray, L.B. Whole-istic biology. Science 295 (2002) 1661.

    CAS  Article  Google Scholar 

  96. Quackenbush, J. Microarrays — guilt by association. Science 302 (2003) 240–241.

    PubMed  CAS  Article  Google Scholar 

  97. Stuart, J.M., Segal, E., Koller, D. and Kim, S.K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302 (2003) 249–255.

    PubMed  CAS  Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Max-Planck-Institute fuer Molekulare Pflanzenphysiologie, Am Muehlenberg 1, 14476, Golm, Germany

    Holger Hesse & Rainer Hoefgen

Authors
  1. Holger Hesse
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rainer Hoefgen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Holger Hesse.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Hesse, H., Hoefgen, R. On the way to understand biological complexity in plants: S-nutrition as a case study for systems biology. Cell. Mol. Biol. Lett. 11, 37–56 (2006). https://doi.org/10.2478/s11658-006-0004-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2478/s11658-006-0004-8

Key words

  • Sulfur metabolism
  • Systems biology
  • Metabolome
  • Ionome
  • Transcriptome
  • Proteome

Cellular & Molecular Biology Letters

ISSN: 1689-1392

Contact us