Trees

, Volume 26, Issue 6, pp 1723–1735 | Cite as

Experimental ‘omics’ data in tree research: facing complexity

Review

Abstract

High-throughput experimental technology has provided insight into the inner functioning of plants. The current experimental technology facilitates the study of plant systems in a holistic manner, measuring observables from the genome, proteome, and metabolome up to the level of the ecosystem. The call for a systemic view in plant research is being made from multiple research fields. Although not yet fully developed for tree research, data sources are also rapidly growing in this area. Nevertheless, there are challenges and pitfalls in dealing with such increases in data. Some of these difficulties are deeply rooted in the complexity of the evolutionary systems. The lessons from complexity theory are rooted in studies performed several decades ago. Honouring principles that were formulated before bioinformatics and systems biology had been introduced facilitates the derivation of analytical methods with the potential to overcome these challenges in several ways.

Keywords

‘Omics’ data Plant systems biology Systems theory Complexity Large-scale modelling 

References

  1. Abbott A (2012) The genome of giants: a walk through the forest of tree genomes. Tree Genet Genomes 8:443CrossRefGoogle Scholar
  2. Abril N, Gion J-M, Kerner R, Müller-Starck G, Navarro Cerrillo RM, Plomion C, Renaut J, Valledor L, Jorrin-Novo JV (2011) Proteomics research on forest trees, the most recalcitrant and orphan plant species. Phytochemistry 72:1219–1242PubMedCrossRefGoogle Scholar
  3. Ahuja I, de Vos RCH, Bones AM, Hall RD (2010) Plant molecular stress responses face climate change. Trends Plant Sci 15:664–674PubMedCrossRefGoogle Scholar
  4. Allesina S, Tang S (2012) Stability criteria for complex ecosystems. Nature 483:205–208PubMedCrossRefGoogle Scholar
  5. Ashby WR (1957) An introduction to cybernetics. Chapman & Hall, LondonGoogle Scholar
  6. Ashby WR (1958) Requisite variety and its implications for the control of complex systems. Cybernetica 1:83–99Google Scholar
  7. Ashby WR (1962) Principles of the self-organizing system. In: von Foerster H, Zopf GW Jr (eds) Principles of self-organization: transactions of the University of Illinois Symposium. Pergamon Press, London, pp 255–278Google Scholar
  8. Baker M (2012) The changes that count. Nature 482:257–262PubMedCrossRefGoogle Scholar
  9. Bao Y, Dharmawardhana P, Mockler TC, Strauss SH (2009) Genome scale transcriptome analysis of shoot organogenesis in Populus. BMC Plant Biol 9:132PubMedCrossRefGoogle Scholar
  10. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512PubMedCrossRefGoogle Scholar
  11. Barabási A-L, Oltavi ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113PubMedCrossRefGoogle Scholar
  12. Bascompte J (2007) Networks in ecology. Basic Appl Ecol 8:485–490CrossRefGoogle Scholar
  13. Beckage B, Gross LJ, Kauffman S (2011) The limits to prediction in ecological systems. Ecosphere 2:125CrossRefGoogle Scholar
  14. Bohler S, Bagard M, Oufir M, Planchon S, Hoffman L, Jolivet Y, Hausmann J-F, Dizengremel P, Renaut J (2007) A DIGE analysis of developing poplar leaves subjected to ozone reveals major changes in carbon metabolism. Proteomics 7:1584–1599PubMedCrossRefGoogle Scholar
  15. Brenner S (1999) Theoretical biology in the third millennium. Phil Trans R Soc Lond B 354:1963–1965CrossRefGoogle Scholar
  16. Brosché M, Vincour B, Alatalo ER et al (2005) Gene expression and metabolite profiling of Populus euphratica growing in the Negev desert. Genome Biol 6:R101PubMedCrossRefGoogle Scholar
  17. Buée M, Reich M, Murat C, Morin E, Nilsson RH, Uroz S, Martin F (2009) 454 pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity. New Phytol 184:449–456PubMedCrossRefGoogle Scholar
  18. Businge E, Brackman K, Moritz T, Egertsdotter U (2012) Metabolite profiling reveals clear metabolic changes during somatic development of Norway spruce (Pices abies). Tree Physiol 32:232–244PubMedCrossRefGoogle Scholar
  19. Bylesjö M, Nilsson R, Srivastava V, Grönlund A, Johansson AI, Jansson S, Karlsson J, Moritz T, Wingsle G, Trygg J (2009) Integrated analysis of transcript, protein and metabolite data to study lignin biosynthesis in hybrid aspen. J Prot Res 8:199–210CrossRefGoogle Scholar
  20. Calfapietra C, Ainsworth EA, Beier C et al (2010) Challenges in elevated CO2 experiments on forests. Trends Plant Sci 15:5–10PubMedCrossRefGoogle Scholar
  21. Cassman M (2005) Barriers to progress in systems biology. Nature 438:1079PubMedCrossRefGoogle Scholar
  22. Castrillo JI, Oliver SG (2004) Yeast as a touchstone in post-genomic research: strategies for integrative analysis in functional genomics. J Biochem Mol Biol 37:93–106PubMedCrossRefGoogle Scholar
  23. Chen F, Liu C-J, Tschaplinski TJ, Zhao N (2012) Genomics of secondary metabolism in Populus: interactions with biotic and abiotic environment. Crit Rev Plant Sci 28:375–392CrossRefGoogle Scholar
  24. Cohen D, Bogeat-Triboulot M-B, Tisserant E, Balzergue S, Martin-Magniette M-L, Lelandais G, Ningre N, Renou J-P, Tamby J-P, Le Thiec D, Hummel I (2010) Comparative transcriptomics of drought responses in Populus: a meta-analysis of genome-wide expression profiling in mature leaves and root apices across two genotypes. BMC Genomics 11:630PubMedCrossRefGoogle Scholar
  25. Corning PA (1997) Holistic Darwinism: ‘Synergistic selection’ and the evolutionary process. J Soc Evol Syst 20:363–400Google Scholar
  26. Courty PE, Poletto M, Duchaussoy F, Buée M, Garbaye J, Martin F (2008) Gene transcription in Lactarius quietus-Quercus petraea ectomycorrhizas from a forest soil. Appl Environ Microbiol 74:6598–6605PubMedCrossRefGoogle Scholar
  27. Csete ME, Doyle JC (2002) Reverse engineering of biological complexity. Science 295:1664–1669PubMedCrossRefGoogle Scholar
  28. Doyle JC, Csete ME (2005) Motifs, control, and stability. PLoS Biol 3:e392PubMedCrossRefGoogle Scholar
  29. Druart N, Johansson A, Baba K, Schrader J, Sjödin A, Bhalerao RR, Resman L, Trygg J, Moritz T, Bhalerao RP (2007) Environmental and hormonal regulation of the activity-dormancy cycle in the cambial meristem involves stage-specific modulation of transcriptional and metabolic networks. Plant J 50:557–573PubMedCrossRefGoogle Scholar
  30. Durand TC, Sergeant K, Renaut J, Planchon S, Hoffmann L, Carpin S, Label P, Morabito D, Hausman JF (2011) Poplar under drought: comparison of leaf and cambial proteomic responses. J Proteomics 74:1396–1410PubMedCrossRefGoogle Scholar
  31. Eckert AJ, Pande B, Ersoz ES, Wright MH, Rashbrook VK, Nicolet CM, Neale DB (2009) High-throughput genotyping and mapping of single nucleotide polymorphisms in loblolly pine (Pinus taeda L.). Tree Genet Genomes 5:225–234CrossRefGoogle Scholar
  32. Eckert AJ, Wegrzyn JL, Cumbie WP, Goldfarb B, Huber DA, Tolstikov V, Fiehn O, Neale DB (2012) Association genetics of the loblolly pine (Pinus taeda, Pinaceae) metabolome. New Phytol 193:890–902PubMedCrossRefGoogle Scholar
  33. Ernst D, Bahnweg G, Heller W (2012a) Effects of abiotic and biotic stress on gene transcription in European beech: from saplings to mature trees. Nova Acta Leopoldina (in press)Google Scholar
  34. Ernst D, Jürgensen M, Bahnweg G, Heller W, Müller-Starck G (2012b) Common links of molecular biology with biochemistry and physiology in plants under ozone and pathogen attack. In: Matyssek R, Schnyder H, Oßwald W, Ernst D, Munch JC, Pretzsch H (eds) Growth and defence in plants: resource allocation at multiple scales. Ecological studies, vol 220. Springer, Heidelberg, pp 29–51Google Scholar
  35. Fernández P, Solé RV (2006) The role of computation in complex regulatory networks. In: Koonin EV, Wolf Y, Karev GP (eds) Power laws, scale-free networks and genome biology. Landes bioscience. Springer, New York, pp 206–225Google Scholar
  36. Fernández-Pozo N, Canales J, Guerrero-Fernández D et al (2011) EuroPineDB: a high-coverage web database for maritime pine transcriptome. BMC Genomics 12:366PubMedCrossRefGoogle Scholar
  37. Fernie AR (2012) Grand challenges in plant systems biology: closing the circle(s). Front Plant Sci 3:35PubMedCrossRefGoogle Scholar
  38. Filichkin SA, Breton G, Priest HD, Dharmawardhana P, Jaiswal P, Fox SE, Michael TP, Chory J, Kay S, Mockler TC (2011) Global profiling of rice and poplar transcriptomes highlights key conserved circadian-controlled pathways and cis-regulatory modules. PLoS One 6:e16907PubMedCrossRefGoogle Scholar
  39. Fox Keller E (2005a) The century beyond the gene. J Biosci 30:3–10CrossRefGoogle Scholar
  40. Fox Keller E (2005b) Revisiting “scale-free” networks. Bioessays 27:1060–1068CrossRefGoogle Scholar
  41. Fukushima A, Kusano M, Redestig H, Arita M, Saito K (2009) Integrated omics approaches in plant systems biology. Curr Opin Chem Biol 13:532–538PubMedCrossRefGoogle Scholar
  42. Galbraith DW (2006) DNA microarray analyses in higher plants. OMICS 10:455–473PubMedCrossRefGoogle Scholar
  43. Galindo González LM, El Kayal W, Ju CJ-T, Allen CCG, King-Jones S, Cooke JEK (2012) Integrated transcriptomic and proteomic profiling of white spruce stems during the transition from active growth to dormancy. Plant Cell Environ 35:682–701PubMedCrossRefGoogle Scholar
  44. Gershenson C (2007) The world as evolving information. In: Minai A, Braha D, Bar-Yam Y (eds) Online Proceedings of the Seventh International Conference on Complex Systems. Paper #17. http://necsi.edu/events/iccs7/papers/9da71337b2793874036e781a0c6c.pdf
  45. Gershenson C (2011a) The sigma profile: a formal tool to study organization and its evolution at multiple scales. Complexity 16:37–44CrossRefGoogle Scholar
  46. Gershenson C (2011b) The implications of interactions for science and philosophy. Cornell University Library. arXiv:1105.2827v1. (http://arxiv.org/pdf/1105.2827.pdf)
  47. Gershenson C, Heylighen F (2003) When can we call a system self-organizing? In: Banzhaf W, Christaller T, Dittrich P, Kim JT, Ziegler J (eds) Advances in artificial life. 7th European Conference, ECAL 2003 LNAI 2801. Springer, Heidelberg, pp 606–614Google Scholar
  48. Gmitter FG, Chen C, Machado MA, Alves de Souza A, Ollitrault P, Froehlicher Y, Shimizu T (2012) Citrus genomics. Tree Genet Genomes 8:611–626CrossRefGoogle Scholar
  49. Godsoe W, Strand E, Smith CI, Yoder JB, Esque TC, Pellmyr O (2009) Divergence in an obligate mutualism is not explained by divergent climatic factors. New Phytol 183:589–599PubMedCrossRefGoogle Scholar
  50. Grattapaglia D, Vaillancourt RE, Shepard M, Thumma BR, Foley W, Külheim C, Potts BM, Myburg AA (2012) Progress in Myrtaceae genetics and genomics: Eucalyptus as the pivotal genus. Tree Genet Genomes 8:463–508CrossRefGoogle Scholar
  51. Gupta P, Duplessis S, White H, Karnosky DF, Martin F, Podila GK (2005) Gene expression patterns of trembling aspen trees following long-term exposure to interacting elevated CO2 and tropospheric O3. New Phytol 167:129–142PubMedCrossRefGoogle Scholar
  52. Haken H (1983) Synergetics, an introduction. In: Nonequilibrium phase-transitions and self-organization in physics, chemistry and biology, 3rd edn. Springer, New YorkGoogle Scholar
  53. Hall DE, Robert JA, Keeling CI, Domanski D, Quesada AL, Jancsik S, Kuzyk MA, Hamberger B, Borchers CH, Bohlmann J (2011) An integrated genomic, proteomic and biochemical analysis of (+)-3-carene biosynthesis in Sitka spruce (Picea sitchensis) genotypes that are resistant or susceptible to white pine weevil. Plant J 65:936–948PubMedCrossRefGoogle Scholar
  54. Heylighen F (1992) Principles of systems and cybernetics: an evolutionary perspective. In: Trappl R (ed) Cybernetics and systems’92. World Science, Singapore, pp 3–10. (http://pcp.lanl.gov/papers/PrinciplesCybSys.pdf)
  55. Hoffman DE, Jonsson P, Bylesiö M, Trygg J, Antti H, Erikkson ME, Moritz T (2010) Changes in diurnal patterns within the Populus transcriptome and metabolome in response to photoperiod variation. Plant Cell Environ 33:1298–1313PubMedGoogle Scholar
  56. Hogeweg P (2011) The roots of bioinformatics in theoretical biology. PLoS Comput Biol 7:e1002021PubMedCrossRefGoogle Scholar
  57. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366CrossRefGoogle Scholar
  58. Isbell F, Calcagno V, Hector A, Connolly J, Harpole WS, Reich PB, Scherer-Lorenzen M, Schmid B, van Ruijven J, Weigelt A, Wilsey BJ, Zavalety ES, Loreau M (2011) High plant diversity is needed to maintain ecosystem services. Nature 477:199–202PubMedCrossRefGoogle Scholar
  59. Jansson S, Douglas CJ (2009) Populus: a model system for plant biology. Annu Rev Plant Biol 58:435–458CrossRefGoogle Scholar
  60. Janz D, Behnke K, Schnitzler J-P, Kanawati B, Schmitt-Kopplin P, Polle A (2010) Pathway analyses of the transcriptome and metabolome of salt sensitive and tolerant poplar species reveals evolutionary adaptation to stress tolerance mechanisms. BMC Plant Biol 10:150PubMedCrossRefGoogle Scholar
  61. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási A-L (2000) The large-scale organization of metabolic networks. Nature 407:651–654PubMedCrossRefGoogle Scholar
  62. Joyce AR, Palsson BØ (2006) The model organism as a system: integrating ‘omics’ data sets. Nat Rev Mol Cell Biol 7:198–210PubMedCrossRefGoogle Scholar
  63. Kauffman S (1969) Metabolic stability and epigenesist in randomly constructed genetic nets. J Theor Biol 22:437–467PubMedCrossRefGoogle Scholar
  64. Kauffman S (1996) At home in the universe: the search for laws of self-organization and complexity. Oxford University Press, New YorkGoogle Scholar
  65. Kauffman S, Clayton P (2006) On emergence, agency, and organization. Biol Philos 21:501–521CrossRefGoogle Scholar
  66. Kerner R, Winkler JB, Dupuy JW, Jürgensen M, Lindermayr C, Ernst D, Müller-Starck G (2010) Changes in the proteome of juvenile European beech following three years exposure to free-air elevated ozone. iForest 4:69–76CrossRefGoogle Scholar
  67. Khaitovich P, Weiss G, Lachmann M, Hellmann I, Enard W, Muetzel B, Wirkner U, Ansorge W, Pääbo S (2004) A neutral model of transcriptome evolution. PLoS Biol 2:0682–0689CrossRefGoogle Scholar
  68. Kieffer P, Planchon S, Oufir M, Ziebel J, Dommes J, Hoffmann L, Hausman JF, Renault J (2009) Combining proteomics and metabolite analyses to unravel cadmium stress-response in poplar leaves. J Prot Res 8:400–417CrossRefGoogle Scholar
  69. Kim J (1999) Making sense of emergence. Philos Stud 95:3–36CrossRefGoogle Scholar
  70. Kitano H (2002) Systems biology: a brief overview. Science 295:1662–1664PubMedCrossRefGoogle Scholar
  71. Kitano H (2004) Biological robustness. Nat Rev Genet 5:826–837PubMedCrossRefGoogle Scholar
  72. Kliebenstein DJ (2010) Systems biology uncovers the foundation of natural genetic diversity. Plant Physiol 152:480–486PubMedCrossRefGoogle Scholar
  73. Kontunen-Soppela S, Ossipov V, Ossipova S, Oksanen E (2007) Shift in birch leaf metabolome and carbon allocation during long-term open-field ozone exposure. Glob Change Biol 13:1053–1067CrossRefGoogle Scholar
  74. Kontunen-Soppela S, Parviainen J, Ruhanen H, Brosché M, Keinänen M, Thakur RC, Kohlemainen M, Kangasjärvi J, Oksanen E, Karnosky DF, Vapaavuori E (2010) Gene expression responses of paper birch (Betula papyrifera) to elevated CO2 and O3 during leaf maturation and senescence. Environ Pollut 158:959–968PubMedCrossRefGoogle Scholar
  75. Kosová K, Vítámvás P, Prášil IT, Renaut J (2011) Plant proteome changes under abiotic stress—contribution of proteomics studies to understanding plant stress responses. J Proteomics 74:1301–1322PubMedCrossRefGoogle Scholar
  76. Kremer A, Abbott AG, Carlson JE, Manos PS, Plomion C, Sisco P, Staton ME, Ueno S, Vendramin GG (2012) Genomics of Fagaceae. Tree Genet Genomes 8:583–601CrossRefGoogle Scholar
  77. Larsen PE, Sreedasyam A, Trivedi G, Podila GK, Cseke LJ, Collart FR (2011) Using next generation transcriptome sequencing to predict an ectomycorrhizal metabolome. BMC Syst Biol 5:70PubMedCrossRefGoogle Scholar
  78. Lay JO Jr, Liyanage R, Borgmann S, Wilkins CL (2006) Problems with the “omics”. Trends Anal Chem 25:1046–1056CrossRefGoogle Scholar
  79. Liu W, Thummasuwan S, Sehgal SK, Chouvarine P, Peterson DG (2011a) Characterization of the genome of bald cypress. BMC Genomics 12:553PubMedCrossRefGoogle Scholar
  80. Liu Y-Y, Slotine J-J, Barabási A-L (2011b) Controllability of complex networks. Nature 473:167–173PubMedCrossRefGoogle Scholar
  81. Lorenz WW, Alba R, Yu YS, Bordeaux JM, Simões M, Dean JFD (2011) Microarray analysis and scale-free gene networks identify candidate regulators in drought-stressed roots of loblolly pine (P. taeda L.). BMC Genomics 12:264PubMedCrossRefGoogle Scholar
  82. Luscombe NM, Babu MM, Yu H, Snyder M, Teichmann SA, Gerstein M (2004) Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431:308–312PubMedCrossRefGoogle Scholar
  83. Lüttge U (2012) Whole-plant physiology: synergistic emergence rather than modularity. Prog Bot 75 (in press)Google Scholar
  84. Martin F, Aerts A, Ahrén D et al (2008) The genome of Laccaria bicolor provides insights into mycorrhizal symbiosis. Nature 452:88–93PubMedCrossRefGoogle Scholar
  85. Matyssek R, Bahnweg G, Ceulemans R, Fabian P, Grill D, Hanke DE, Kraigher H, Oßwald W, Rennenberg H, Sandermann H, Tausz M, Wieser G (2007) Synopsis of the CASIROZ case study: carbon sink strength of Fagus sylvatica L. in a changing environment—experimental risk assessment of mitigation by chronic ozone impact. Plant Biol 9:163–180PubMedCrossRefGoogle Scholar
  86. May RM (2001) Stability and complexity in model ecosystems. Princeton University Press, PrincetonGoogle Scholar
  87. Maynard Smith J, Szathmáry E (1995) The major transitions in evolution. WH Freeman, New YorkGoogle Scholar
  88. Mazzochi F (2008) Complexity in biology. EMBO Rep 9:10–14CrossRefGoogle Scholar
  89. Medina M, Sachs JL (2010) Symbiont genomics, our new tangled bank. Genomics 95:129–137PubMedCrossRefGoogle Scholar
  90. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298:824–827PubMedCrossRefGoogle Scholar
  91. Mochida K, Shinozaki K (2011) Advances in omics and bioinformatics tools for systems analyses of plant functions. Plant Cell Physiol 52:2017–2038PubMedCrossRefGoogle Scholar
  92. Morgenstern O, von Neumann J (1944) The theory of games and economic behavior. Princeton University Press, PrincetonGoogle Scholar
  93. Morreel K, Goeminne G, Storme V, Sterck L, Ralph J, Coppieters W, Breyne P, Steenackers M, Georges M, Messens E, Boerjan W (2006) Genetical metabolomics of flavonoid biosynthesis in Populus: a case study. Plant J 47:224–237PubMedCrossRefGoogle Scholar
  94. Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256CrossRefGoogle Scholar
  95. Ng A, Bursteinas B, Gao Q, Mollison E, Zvelebil M (2006) Resources for integrative systems biology: from data through databases to networks and dynamic system models. Briefings Bioinform 7:318–330CrossRefGoogle Scholar
  96. Nicolis G, Prigogine I (1977) Self-organization in non-equilibrium systems. Wiley, New YorkGoogle Scholar
  97. Noble D (2002) Modeling the heart—from genes to cells to the whole organ. Science 295:1678–1682PubMedCrossRefGoogle Scholar
  98. Nurse P (2008) Life, logic and information. Nature 454:424–426PubMedCrossRefGoogle Scholar
  99. Olbrich M, Gerstner E, Welzl G, Winkler JB, Ernst D (2009) Transcript responses in leaves of ozone-treated beech saplings at an outdoor free air model fumigation site over two growing seasons. Plant Soil 323:61–74CrossRefGoogle Scholar
  100. Olbrich M, Knappe C, Wenig M et al (2010) Ozone fumigation (twice ambient) reduces leaf infestation following natural and artificial inoculation by the endophytic fungus Apiognomonia errabunda of adult European beech trees. Environ Pollut 158:1043–1050PubMedCrossRefGoogle Scholar
  101. Ossipov V, Ossipova S, Bykov V, Oksanen E, Koricheva J, Haukioja E (2008) Application of metabolomics to genotype and phenotype discrimination of birch trees grown in a long-term field experiment. Metabolomics 4:39–51CrossRefGoogle Scholar
  102. Parchman TL, Geist KS, Grahnen JA, Benkman CW, Buerkle CA (2010) Transcriptome sequencing in ecologically important tree species: assembly, annotation, and marker discovery. BMC Genomics 11:180PubMedCrossRefGoogle Scholar
  103. Pavy N, Johnson JJ, Crow JA, Paule C, Kunau T, Mackay J, Retzel EF (2007) Forest TreeDB: a database dedicated to the mining of tree transcriptomes. Nucleic Acids Res 35:D887–D894CrossRefGoogle Scholar
  104. Pawlowski TA (2010) Proteomic approach to analyze dormancy breaking of tree seeds. Plant Mol Biol 73:15–25PubMedCrossRefGoogle Scholar
  105. Pfeiffer T, Hoffmann R (2009) Large-scale assessment of the effect of popularity on the reliability of research. PLoS One 4:e5996PubMedCrossRefGoogle Scholar
  106. Plomion C, Lalanne C, ClaveroL S, Meddour H, Kohler A, Bogeat-Triboulot M-B, Barre A, Le Provost G, Dumazet H, Jacob D, Bastien C, Dreyer E, de Daruvar A, Guehl J-M, Schmitter J-M, Martin F, Bonneu M (2006) Mapping the proteome of poplar and application to the discovery of drought-stress responsive proteins. Proteomics 6:6509–6527PubMedCrossRefGoogle Scholar
  107. Podila GK, Sreedasyam A, Muratet MA (2009) Populus rhizosphere and the ectomycorrhizal interactome. Crit Rev Plant Sci 5:359–367CrossRefGoogle Scholar
  108. Pop A, Huttenhower C, Iyer-Pascuzzi A, Benfey PN, Troyanskaya OG (2010) Integrated functional networks of process, tissue, and development state specific interactions in Arabidopsis thaliana. BMC Syst Biol 4:180PubMedCrossRefGoogle Scholar
  109. Prigogine I, Stengers I (1984) Order out of chaos: man’s new dialogue with nature. Heinemann, LondonGoogle Scholar
  110. Proulx SR, Promislow DEL, Phillips PC (2005) Network thinking in ecology and evolution. Trends Ecol Evol 20:345–353PubMedCrossRefGoogle Scholar
  111. Qiu Q, Ma T, Hu Q, Liu B, Wu Y, Zhou H, Wang Q, Wang J, Liu J (2011) Genome-scale transcriptome analysis of the desert poplar, Populus euphratica. Tree Physiol 31:452–461PubMedCrossRefGoogle Scholar
  112. Ralph SG, Chun HJE, Kolosova N et al (2008) A conifer genomics resource of 200,000 spruce (Picea spp.) ESTs and 6,464 high-quality, sequence-finished full-length cDNA for Sitka spruce (Picea sitchensis). BMC Genomics 9:484PubMedCrossRefGoogle Scholar
  113. Ransohoff DF (2005) Bias as a threat to the validity of cancer molecular-marker research. Nat Rev Cancer 5:142–149PubMedCrossRefGoogle Scholar
  114. Ricardo CPP, Martins I, Francisco R, Sergeant K, Pinheiro C, Campos A, Renaut J, Fevereiro P (2011) Proteins associated with cork formation in Quercus suber L. stem tissue. J Proteomics 74:1266–1278PubMedCrossRefGoogle Scholar
  115. Rigault P, Boyle B, Lepage P, Cooke JEK, Bousquet J, MacKay JJ (2011) A white spruce gene catalog for conifer genome analyses. Plant Physiol 157:14–28PubMedCrossRefGoogle Scholar
  116. Rosenblueth A, Wiener N (1945) The role of models in science. Philos Sci 12:316–321CrossRefGoogle Scholar
  117. Sauer U, Heinemann M, Zamboni N (2007) Getting closer to the whole picture. Science 316:550–551PubMedCrossRefGoogle Scholar
  118. Scherling C, Ulrich K, Ewald D, Weckwerth W (2009) A metabolic signature of the beneficial interaction of the endophyte Paenibacillus sp. isolate and in vitro-grown poplar plants revealed by metabolomics. Mol Plant Microb Interact 22:1032–1037CrossRefGoogle Scholar
  119. Sergeant K, Spieß N, Renault J, Wilhelm E, Hausman JF (2011) One dry summer: a leaf proteome study on the response of oak to drought exposure. J Proteomics 74:1385–1395PubMedCrossRefGoogle Scholar
  120. Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, UrbanaGoogle Scholar
  121. Shulaev V, Cortes D, Miller G, Mittler R (2008) Metabolomics for plant stress response. Physiol Plant 132:199–208PubMedCrossRefGoogle Scholar
  122. Sinclair TR, Purcell LC (2005) Is a physiological perspective relevant in a ‘genocentric’ age? J Exp Bot 56:2777–2782PubMedCrossRefGoogle Scholar
  123. Smale S (1976) On the differential equations of species in competition. J Math Biol 3:5–7PubMedCrossRefGoogle Scholar
  124. Sober E (1981) The principle of parsimony. Brit J Phil Sci 32:145–156CrossRefGoogle Scholar
  125. Sontag ED (2004) Some new directions in control theory inspired by systems biology. Syst Biol 1:9–18CrossRefGoogle Scholar
  126. Soto AM, Sonnenschein C (2006) Emergentism by default: a view from the bench. Synthese 151:361–376CrossRefGoogle Scholar
  127. Street N, Jansson S, Hvidsten TR (2011) A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation. BMC Plant Biol 11:13PubMedCrossRefGoogle Scholar
  128. Sweetlove LJ, Fernie AR (2005) Regulation of metabolic networks: understanding metabolic complexity in the systems biology era. New Phytol 168:9–24PubMedCrossRefGoogle Scholar
  129. Taylor G, Street NR, Tricker PJ, Sjödin A, Graham L, Skogström O, Calfapietra C, Scarascia-Mugnozza G, Jansson S (2005) The transcriptome of Populus in elevated CO2. New Phytol 167:143–154PubMedCrossRefGoogle Scholar
  130. Troggio M, Gleave A, Salvi S, Chagné D, Cestaro A, Kumar S, Crowhurst RN, Gardiner SE (2012) Apple, from genome to breeding. Tree Genet Genomes 8:509–529CrossRefGoogle Scholar
  131. Turing A (1952) The chemical basis of morphogenesis. Phil Trans Roy Soc B 237:37–72CrossRefGoogle Scholar
  132. Tuskan GA, DiFazio S, Jansson S et al (2006) The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 313:1596–1604PubMedCrossRefGoogle Scholar
  133. Ueno S, Le Provost G, Léger V et al (2010) Bioinformatic analysis of ESTs collected by Sanger and pyrosequencing methods for a keystone forest tree species: oak. BMC Genomics 1:650CrossRefGoogle Scholar
  134. Urano K, Kurihara Y, Seki M, Shinozaki K (2010) ‘Omics’ analyses of regulatory networks in plant abiotic stress responses. Curr Opinion Plant Biol 13:132–138CrossRefGoogle Scholar
  135. Valero Galván J, Valledor L, Navarro Cerrillo RM, Pelegrín EG, Jorrín-Novo JV (2011) Studies of variability in holm oak (Quercus ilex subsp. ballota [Desf.] Samp.) through acorn protein profile analysis. J Proteomics 74:1244–1255PubMedCrossRefGoogle Scholar
  136. van Regenmortel MHV (2004) Reductionism and complexity in molecular biology. EMBO Rep 5:1016–1020PubMedCrossRefGoogle Scholar
  137. von Bertalanffy L (1969) General system theory: foundations, development, applications. Braziller, New YorkGoogle Scholar
  138. von Neumann J (1966) Theory of self-reproducing automata. University of Illinois Press, UrbanaGoogle Scholar
  139. Wacholder S, Chanock S, Garcia-Closas M, Elghormli L, Rothman N (2004) Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 96:434–442PubMedCrossRefGoogle Scholar
  140. Warren CR, Aranda I, Cano FJ (2012) Metabolomics demonstrates divergent responses of two Eucalyptus species to water stress. Metabolomics 8:186–200CrossRefGoogle Scholar
  141. Weckwerth W (2011) Green systems biology—from single genomes, proteomes and metabolomes to ecosystems research and biotechnology. J Proteom 75:284–305CrossRefGoogle Scholar
  142. Westerhoff HV, Palsson BO (2004) The evolution of molecular biology into systems biology. Nat Biotech 22:1249–1252CrossRefGoogle Scholar
  143. Wiener N (1948) Cybernetics, or control and communication in the animal and the machine. MIT Press, CambridgeGoogle Scholar
  144. Wullschleger SD, Weston DJ, Davis JM (2009) Populus responses to edaphic and climatic cues: emerging evidence from systems biology research. Crit Rev Plant Sci 28:368–374CrossRefGoogle Scholar
  145. Yang X, Kalluri UD, DiFazio SP, Wullschleger SD, Tschaplinski TJ, Cheng Z-M, Tuskan GA (2009) Poplar genomics: state of the science. Crit Rev Plant Sci 28:285–308CrossRefGoogle Scholar
  146. Yuan SJ, Galbraith DW, Dai SY, Griffin P, Stewart CN Jr (2008) Plant systems biology comes of age. Trends Plant Sci 13:165–171PubMedCrossRefGoogle Scholar
  147. Zilber-Rosenberg I, Rosenberg E (2008) Role of microorganisms in the evolution of animals and plants: the hologenome theory of evolution. FEMS Microbiol Rev 32:723–735PubMedCrossRefGoogle Scholar
  148. zu Castell W (2012) Complex systems: chances and risks for experimental data analysis. Nova Acta Leopoldina (in press)Google Scholar
  149. Zulak KG, Lippert DN, Kuzyk MA, Domanski D, Chou T, Borchers CH, Bohlmann J (2009) Targeted proteomics using selected reaction monitoring reveals the induction of specific terpene synthases in a multi-level study of methyl jasmonate-treated Norway spruce (Picea abies). Plant J 60:1015–1030PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Scientific Computing Research UnitHelmholtz Zentrum MünchenNeuherbergGermany
  2. 2.Institute of Plant PathologyHelmholtz Zentrum MünchenNeuherbergGermany

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