Network visualization and network analysis

  • Victoria J. Nikiforova
  • Lothar Willmitzer
Part of the Experientia Supplementum book series (EXS, volume 97)


Network analysis of living systems is an essential component of contemporary systems biology. It is targeted at assemblance of mutual dependences between interacting systems elements into an integrated view of whole-system functioning. In the following chapter we describe the existing classification of what is referred to as biological networks and show how complex interdependencies in biological systems can be represented in a simpler form of network graphs. Further structural analysis of the assembled biological network allows getting knowledge on the functioning of the entire biological system. Such aspects of network structure as connectivity of network elements and connectivity degree distribution, degree of node centralities, clustering coefficient, network diameter and average path length are touched. Networks are analyzed as static entities, or the dynamical behavior of underlying biological systems may be considered. The description of mathematical and computational approaches for determining the dynamics of regulatory networks is provided. Causality as another characteristic feature of a dynamically functioning biosystem can be also accessed in the reconstruction of biological networks; we give the examples of how this integration is accomplished. Further questions about network dynamics and evolution can be approached by means of network comparison. Network analysis gives rise to new global hypotheses on systems functionality and reductionist findings of novel molecular interactions, based on the reliability of network reconstructions, which has to be tested in the subsequent experiments. We provide a collection of useful links to be used for the analysis of biological networks.


Network Analysis Metabolic Network Biological Network Betweenness Centrality Protein Interaction Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Nikiforova VJ, Kopka J, Tolstikov V, Fiehn O, Hopkins L, Hawkesford MJ, Hesse H, Hoefgen R (2005) Systems re-balancing of metabolism in response to sulfur deprivation, as revealed by metabolome analysis of Arabidopsis plants. Plant Physiol 138: 304–318PubMedCrossRefGoogle Scholar
  2. 2.
    Karp PD, Ouzounis CA, Moore-Kochlacs C, Goldovsky L, Kaipa P, Ahren D, Tsoka S, Darzentas N, Kunin V, Lopez-Bigas N (2005) Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res 33: 6083–6089PubMedCrossRefGoogle Scholar
  3. 3.
    Zhang PF, Foerster H, Tissier CP, Mueller L, Paley S, Karp PD, Rhee SY (2005) MetaCyc and AraCyc. Metabolic pathway databases for plant research. Plant Physiol 138: 27–37PubMedCrossRefGoogle Scholar
  4. 4.
    Krieger CJ, Zhang PF, Mueller LA, Wang A, Paley S, Arnaud M, Pick J, Rhee SY, Karp PD (2004) MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res 32: D438–D442PubMedCrossRefGoogle Scholar
  5. 5.
    Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 27: 29–34PubMedCrossRefGoogle Scholar
  6. 6.
    Sweetlove L, Fernie AR (2005) Tansley Review: Regulation of metabolic networks. Understanding metabolic complexity in the systems biology era. New Phytol 168: 9–23PubMedCrossRefGoogle Scholar
  7. 7.
    Salgado H, Gama-Castro S, Martinez-Antonio A, Diaz-Peredo E, Sanchez-Solano F, Peralta-Gil M, Garcia-Alonso D, Jimenez-Jacinto V, Santos-Zavaleta A, Bonavides-Martinez C et al. (2004) RegulonDB (version 4.0): transcriptional regulation, operon organization and growth conditions in Escherichia coli K-12. Nucleic Acids Res 32: D303–D306PubMedCrossRefGoogle Scholar
  8. 8.
    Alfarano C, Andrade CE, Anthony K, Bahroos N, Bajec M, Bantoft K, Betel D, Bobechko B, Boutilier K, Burgess E et al. (2005) The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res 33: D418–D424PubMedCrossRefGoogle Scholar
  9. 9.
    Shen-Orr SS, Milo RM, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genet 31: 64–68PubMedCrossRefGoogle Scholar
  10. 10.
    Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I et al. (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298: 799–804PubMedCrossRefGoogle Scholar
  11. 11.
    Zhong JH, Zhang HM, Stanyon CA, Tromp G, Finley RL (2003) A strategy for constructing large protein interaction maps using the yeast two-hybrid system: Regulated arrays and two-phase mating. Genome Res 13: 2691–2699PubMedCrossRefGoogle Scholar
  12. 12.
    Cusick ME, Klitgord N, Vidal M, Hill DE (2005) Interactome: gateway into systems biology. Hum Mol Gen 14: R171–R181PubMedCrossRefGoogle Scholar
  13. 13.
    Skipper M (2005) A protein network of one’s own proteins. Nature Rev Mol Cell Biol 6: 824–825CrossRefGoogle Scholar
  14. 14.
    Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P et al. (2000) A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403: 623–627PubMedCrossRefGoogle Scholar
  15. 15.
    Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao YL, Ooi CE, Godwin B, Vitols E et al. (2003) A protein interaction map of Drosophila melanogaster. Science 302: 1727–1736PubMedCrossRefGoogle Scholar
  16. 16.
    Hoebeke M, Chiapello H, Noirot P, Bessieres P (2001) SPiD: a subtilis protein interaction database. Bioinformatics 17: 1209–121PubMedCrossRefGoogle Scholar
  17. 17.
    Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, Vidalain PO, Han JD, Chesneau A, Hao T et al. (2004) A map of the interactome network of the metazoan C. elegans. Science 303: 540–543PubMedCrossRefGoogle Scholar
  18. 18.
    LaCount DJ, Vignali M, Chettier R, Phansalkar A, Bell R, Hesselberth JR, Schoenfeld LW, Ota I, Sahasrabudhe S, Kurschner C et al. (2005) A protein interaction network of the malaria parasite Plasmodium falciparum. Nature 438: 103–107PubMedCrossRefGoogle Scholar
  19. 19.
    Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S et al. (2005) A human protein-protein interaction network: a resource for annotating the proteome. Cell 122: 957–968PubMedCrossRefGoogle Scholar
  20. 20.
    Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N et al. (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature 437: 1173–1178PubMedCrossRefGoogle Scholar
  21. 21.
    de Folter S, Immink RGH, Kieffer M, Parenicova L, Henz SR, Weigel D, Busscher M, Kooiker M, Colombo L, Kater MM et al. (2005) Comprehensive interaction map of the Arabidopsis MADS box transcription factors. Plant Cell 17: 1424–1433PubMedCrossRefGoogle Scholar
  22. 22.
    Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D (2004) The database of interacting proteins: 2004 update. Nucleic Acids Res 32: D449–451PubMedCrossRefGoogle Scholar
  23. 23.
    Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, Rudnev D, Lash AE, Fujibuchi W, Edgar R (2005) NCBI GEO: mining millions of expression profiles — database and tools. Nucleic Acids Res 33: D562–566PubMedCrossRefGoogle Scholar
  24. 24.
    Parkinson H, Sarkans U, Shojatalab M, Abeygunawardena N, Contrino S, Coulson R, Farne A, Garcia Lara G, Holloway E, Kapushesky M et al. (2005) ArrayExpress — a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 33: D553–D555PubMedCrossRefGoogle Scholar
  25. 25.
    Fellenberg K, Hauser NC, Brors B, Hoheisel JD, Vingron M. (2002) Microarray data warehouse allowing for inclusion of experiment annotations in statistical analysis. Bioinformatics 18: 423–433PubMedCrossRefGoogle Scholar
  26. 26.
    Le Crom S, Devaux F, Jacq C, Marc P (2002) yMGV: helping biologists for yeast microarray data mining. Nucleic Acid Res 30: 76–79PubMedCrossRefGoogle Scholar
  27. 27.
    Zimmermann F, Hirsch-Hoffmann M, Hennig L, Gruissem W (2004) GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox. Plant Physiol 136: 2621–2632PubMedCrossRefGoogle Scholar
  28. 28.
    Seki M, Narusaka M, Ishida J, Nanjo T, Fujita M, Oono Y, Kamiya A, Nakajima M, Enju A, Sakurai T et al. (2002) Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold, and high-salinity stresses using a full-length cDNA microarray. Plant J 31: 279–292PubMedCrossRefGoogle Scholar
  29. 29.
    Oliver S (1996) A network approach to the systematic analysis of yeast gene function. Trends in Genetics 12: 241–242PubMedCrossRefGoogle Scholar
  30. 30.
    Hodgman TC (2000) A historical perspective on gene/protein functional assignment. Bioinformatics 16: 10–15PubMedCrossRefGoogle Scholar
  31. 31.
    Blochzupan A, Decimo D, Loriot M, Mark MP, Ruch JV (1994) Expression of nuclear retinoic acid receptors during mouse odontogenesis. Differentiation 57: 195–203CrossRefGoogle Scholar
  32. 32.
    Yamazaki M, Majeska RJ, Yoshioka H, Moriya H, Einhorn TA (1997) Spatial and temporal expression of fibril-forming minor collagen genes (types V and XI) during fracture healing. J Orthopaedic Res 15: 757–764CrossRefGoogle Scholar
  33. 33.
    Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman, Lockhart DJ et al. (1998) A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 2: 65–73PubMedCrossRefGoogle Scholar
  34. 34.
    Zhang MQ (1999) Promoter analysis of co-regulated genes in the yeast genome. Comput Chem 23: 233–250PubMedCrossRefGoogle Scholar
  35. 35.
    Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95: 14863PubMedCrossRefGoogle Scholar
  36. 36.
    Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO, Herskowitz I (1998) The transcriptional program of sporulation in budding yeast. Science 282: 699–705PubMedCrossRefGoogle Scholar
  37. 37.
    Kim SK, Lund J, Kiraly M, Duke K, Jiang M, Stuart JM, Eizinger A, Wylie BN, Davidson GS (2001) A gene expression map for Caenorhabditis elegans. Science 293: 2087PubMedCrossRefGoogle Scholar
  38. 38.
    Stuart JM, Segal E, Koller D, Kim SK (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302: 249–255PubMedCrossRefGoogle Scholar
  39. 39.
    Snel B, van Noort V, Huynen MA (2004) Gene co-regulation is highly conserved in the evolution of eukaryotes and prokaryotes. Nucleic Acids Res 32: 4725–4731PubMedCrossRefGoogle Scholar
  40. 40.
    Choi JK, Yu US, Yoo OJ, Kim S (2005) Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21: 4348–4355PubMedCrossRefGoogle Scholar
  41. 41.
    Kose F, Weckwerth W, Linke T, Fiehn O (2001) Visualizing plant metabolomic correlation networks using clique-metabolite matrices. Bioinformatics 17: 1198–1208PubMedCrossRefGoogle Scholar
  42. 42.
    Steuer R, Kurths J, Fiehn O, Weckwerth W (2003) Observing and interpreting correlations in metabolomic networks. Bioinformatics 19: 1019–1026PubMedCrossRefGoogle Scholar
  43. 43.
    Askenazi M, Driggers EM, Holtzman DA, Norman TC, Iverson S, Zimmer DP, Boers ME, Blomquist PR, Martinez EJ, Monreal AW et al. (2003) Integrating transcriptional and metabolite profiles to direct the engineering of lovastatin-producing fungal strains. Nature Biotechnol 21: 150–156CrossRefGoogle Scholar
  44. 44.
    Urbanczyk-Wochniak E, Luedemann A, Kopka J, Selbig J, Roessner-Tunali U, Willmitzer L, Fernie AR (2003) Parallel analysis of transcript and metabolic profiles: a new approach in systems biology. EMBO Rep 4: 989–993PubMedCrossRefGoogle Scholar
  45. 45.
    Hirai MY, Yano M, Goodenowe DB, Kanaya S, Kimura T, Awazuhara M, Arita M, Fujiwara T, Saito K (2004) Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci USA 101: 10205–10210PubMedCrossRefGoogle Scholar
  46. 46.
    Hirai MY, Klein M, Fujikawa Y, Yano M, Goodenowe DB, Yamazaki Y, Kanaya S, Nakamura Y, Kitayama M, Suzuki H et al. (2005) Elucidation of gene-to-gene and metaboliteto-gene networks in Arabidopsis by integration of metabolomics and transcriptomics. J Biol Chem 280: 25590–25595PubMedCrossRefGoogle Scholar
  47. 47.
    Nikiforova VJ, Daub CO, Hesse H, Willmitzer L, Hoefgen R (2005) Integrative genemetabolite network with implemented causality deciphers informational fluxes of sulphur stress response. J Exp Bot 56: 1887–1896PubMedCrossRefGoogle Scholar
  48. 48.
    Weckwerth W, Morgenthal K (2005) Metabolomics: from pattern recognition to biological interpretation. Drug Discov Today 10: 1551–1558PubMedCrossRefGoogle Scholar
  49. 49.
    Weckwerth W, Loureiro ME, Wenzel K, Fiehn O (2004) Differential metabolic networks unravel the effects of silent plant phenotypes. Proc Natl Acad Sci USA 101: 7809–7814PubMedCrossRefGoogle Scholar
  50. 50.
    Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B (1998) Comprehensive identification of cell cycle-regulated gene of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9: 3273–3297PubMedGoogle Scholar
  51. 51.
    Vlieghe K, Vuylsteke M, Florquin K, Rombauts S, Maes S, Ormenese S, Van Hummelen P, Van de Peer Y, Inze D, De Veylder L (2003) Microarray analysis of E2Fa-DPa-overexpressing plants uncovers a cross-talking genetic network between DNA replication and nitrogen assimilation. J Cell Sci 116: 4249–4259PubMedCrossRefGoogle Scholar
  52. 52.
    Liu FL, VanToai T, Moy LP, Bock G, Linford LD, Quackenbush J (2005) Global transcription profiling reveals comprehensive insights into hypoxic response in Arabidopsis. Plant Physiol 137: 1115–1129PubMedCrossRefGoogle Scholar
  53. 53.
    Venter M, Botha FC (2004) Promoter analysis and transcription profiling: Integration of genetic data enhances understanding of gene expression. Physiol Plant 120: 74–83PubMedCrossRefGoogle Scholar
  54. 54.
    Xia Y, Yu HY, Jansen R, Seringhaus M, Baxter S, Greenbaum D, Zhao HY, Gerstein M (2004) Analyzing cellular biochemistry in terms of molecular networks. Annu Rev Biochem 73: 1051–1087PubMedCrossRefGoogle Scholar
  55. 55.
    Schlitt T, Brazma A (2005) Modelling gene networks at different organisational levels. FEBS Letters 579: 1859–1866PubMedCrossRefGoogle Scholar
  56. 56.
    Kollmann M, Lovdok L, Bartholome K, Timmer J, Sourjik V (2005) Design principles of a bacterial signalling network. Nature 438: 504–507PubMedCrossRefGoogle Scholar
  57. 57.
    Bagnato A, Spinella F, Rosano L (2005) Emerging role of the endothelin axis in ovarian tumor progression. Endocr Relat Cancer 12: 761–772PubMedCrossRefGoogle Scholar
  58. 58.
    Kundu JK, Surh YJ (2005) Breaking the relay in deregulated cellular signal transduction as a rationale for chemoprevention with anti-inflammatory phytochemicals. Mutat Res — Fund Mol Mech Mut 591: 123–146Google Scholar
  59. 59.
    Katagiri F (2004) A global view of defense gene expression regulation — a highly interconnected signaling network. Curr Opin Plant Biol 7: 506–511PubMedCrossRefGoogle Scholar
  60. 60.
    Zhao J, Davis LC, Verpoorte R (2005) Elicitor signal transduction leading to production of plant secondary metabolites. Biotech Adv 23: 283–333CrossRefGoogle Scholar
  61. 61.
    Feechan A, Kwon E, Yun BW, Wang YQ, Pallas JA, Loake GJ (2005) A central role for S-nitrosothiols in plant disease resistance. Proc Natl Acad Sci USA 102: 8054–8059PubMedCrossRefGoogle Scholar
  62. 62.
    Gechev TS, Minkov IN, Hille J (2005) Hydrogen peroxide-induced cell death in Arabidopsis: Transcriptional and mutant analysis reveals a role of an oxoglutarate-dependent dioxygenase gene in the cell death process. IUBMB Life 57: 181–188PubMedCrossRefGoogle Scholar
  63. 63.
    Murata Y, Pei ZM, Mori IC, Schroeder J (2001) Abscisic acid activation of plasma membrane Ca2+ channels in guard cells requires cytosolic NAD(P)H and is differentially disrupted upstream and downstream of reactive oxygen species production in abi1-1 and abi2-1 protein phosphatase 2C mutants. Plant Cell 13: 2513–2523PubMedCrossRefGoogle Scholar
  64. 64.
    MacRobbie EAC (2002) Evidence for a role for protein tyrosine phosphatase in the control of ion release from the guard cell vacuole in stomatal closure. Proc Natl Acad Sci USA 99: 11963–11968PubMedCrossRefGoogle Scholar
  65. 65.
    Mustilli AC, Merlot S, Vavasseur A, Fenzi F, Giraudat J (2002) Arabidopsis OST1 protein kinase mediates the regulation of stomatal aperture by abscisic acid and acts upstream of reactive oxygen species production. Plant Cell 14: 3089–3099PubMedCrossRefGoogle Scholar
  66. 66.
    Bright J, Desikan R, Hancock JT, Weir IS, Neill SJ (2006) ABA-induced NO generation and stomatal closure in Arabidopsis are dependent on H2O2 synthesis. Plant J 45: 113–122PubMedCrossRefGoogle Scholar
  67. 67.
    Janes KA, Albeck JG, Gaudet S, Sorger PK, Lauffenburger DA, Yaffe MB (2005) Systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310: 1646–1653PubMedCrossRefGoogle Scholar
  68. 68.
    de la Fuente A, Brazhnik P, Mendes P (2002) Linking the genes: inferring quantitative gene networks from microarray data. Trends Genetics 18: 395–398CrossRefGoogle Scholar
  69. 69.
    Mazhawidza W, Winters SJ, Kaiser UB, Kakar SS (2006) Identification of gene networks modulated by activin in L beta T2 cells using DNA microarray analysis. Histol Histopathol 21: 167–178PubMedGoogle Scholar
  70. 70.
    Chan ZSH, Kasabov N, Collins L (2006) A two-stage methodology for gene regulatory network extraction from time-course gene expression data. Expert Systems with Applications 30: 59–63CrossRefGoogle Scholar
  71. 71.
    Costa MMR, Fox S, Hanna AI, Baxter C, Coen E (2005) Evolution of regulatory interactions controlling floral asymmetry. Development 132: 5093–5101PubMedCrossRefGoogle Scholar
  72. 72.
    Zhang R, Ou HY, Zhang CT (2004) DEG, a Database of Essential Genes. Nucleic Acids Res 32: D271–D272PubMedCrossRefGoogle Scholar
  73. 73.
    Apic G, Gough J, Teichmann SA (2001) Domain combinations in archaeal, eubacterial and eukaryotic proteomes. J Mol Biol 310: 311–325PubMedCrossRefGoogle Scholar
  74. 74.
    Dokholyan NV, Shakhnovich B, Shakhnovich EI (2002) Expanding protein universe andits origin from the biological Big Bang. Proc Natl Acad Sci USA 99: 14132–14136PubMedCrossRefGoogle Scholar
  75. 75.
    Garten Y, Kaplan S, Pilpel Y (2005) Extraction of transcription regulatory signals from genome-wide DNA — protein interaction data. Nucleic Acids Res 33: 605–615PubMedCrossRefGoogle Scholar
  76. 76.
    Yu T, Li K-C (2005) Inference of transcriptional regulatory network by two-stage constrained space factor analysis. Bioinformatics 21: 4033–4038PubMedCrossRefGoogle Scholar
  77. 77.
    Lu LJ, Xia Y, Paccanaro A, Yu H, Gerstein M (2005) Assessing the limits of genomic data integration for predicting protein networks. Genome Res 15: 945–953PubMedCrossRefGoogle Scholar
  78. 78.
    Patil KR, Nielsen J (2005) Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci USA 102: 2685–2689PubMedCrossRefGoogle Scholar
  79. 79.
    de Lichtenberg U, Jensen LJ, Brunak S, Bork P (2005) Dynamic complex formation during the yeast cell cycle. Science 307: 724–727PubMedCrossRefGoogle Scholar
  80. 80.
    Ihmels J, Levy R, Barkai N (2004) Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae. Nat Biotechnol 22: 86–92PubMedCrossRefGoogle Scholar
  81. 81.
    Yeang CH, Ideker T, Jaakkola T (2004) Physical network models. J Comput Biol 11: 243–262PubMedCrossRefGoogle Scholar
  82. 82.
    Kitano H, Funahashi A, Matsuoka Y, Oda K (2005) Using process diagrams for the graphical representation of biological networks. Nat Biotechnol 23: 961–966PubMedCrossRefGoogle Scholar
  83. 83.
    Pirson I, Fortemaison N, Jacobs C, Dremier S, Dumont JE, Maenhaut C (2000) The visual display of regulatory information and networks. Trends Cell Biol 10: 404–408PubMedCrossRefGoogle Scholar
  84. 84.
    Kohn KW (2001) Molecular interaction maps as information organizers and simulation guides. Chaos 11: 84–97PubMedCrossRefGoogle Scholar
  85. 85.
    Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A et al. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19: 524–531PubMedCrossRefGoogle Scholar
  86. 86.
    Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286: 509–512CrossRefPubMedGoogle Scholar
  87. 87.
    Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi A-L (2000) The large-scale organization of metabolic networks. Nature 407: 651–654PubMedCrossRefGoogle Scholar
  88. 88.
    Watts DJ, Strogatz SH (1998) Collective dynamics of’ small-world’ networks. Nature 393: 440–442PubMedCrossRefGoogle Scholar
  89. 89.
    Albert R, Jeong H, Barabási AL (2000) Error and attack tolerance of complex networks. Nature 406: 378–382CrossRefPubMedGoogle Scholar
  90. 90.
    Stumpf MPH, Ingram PJ (2005) Probability models for degree distributions of protein interaction networks. Europhysics Letters 71: 152–158CrossRefGoogle Scholar
  91. 91.
    Arita M (2005) Scale-freeness and biological networks. J Biochem 138: 1–4PubMedCrossRefGoogle Scholar
  92. 92.
    Borgatti SP (1995) Centrality and AIDS. Connections 18: 112–115Google Scholar
  93. 93.
    Estrada E, Rodriguez-Velazquez JA (2005) Subgraph centrality in complex networks. Physical Review E 7105: 6103Google Scholar
  94. 94.
    Freeman LC (1979) Centrality in social networks conceptual clarification. Soc Networks 1: 215–239CrossRefGoogle Scholar
  95. 95.
    Albert R, Jeong H, Barabasi AL (1999) Internet — Diameter of the World-Wide Web. Nature 401: 130–131CrossRefGoogle Scholar
  96. 96.
    Bonacich P (1972) Factoring and weighting approaches to status scores and clique identification. J Math Sociol 2: 113–120CrossRefGoogle Scholar
  97. 97.
    Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40: 35–41CrossRefGoogle Scholar
  98. 98.
    Hagen G, Killinger DK, Streeter RB (1997) An analysis of communication networks among Tampa Bay economic development organizations. Connections 20: 13–22Google Scholar
  99. 99.
    Wuchty S, Stadler PF (2003) Centers of complex networks. J Theor Biol 223: 45–53PubMedCrossRefGoogle Scholar
  100. 100.
    Coulomb S, Bauer M, Bernard D, Marsolier-Kergoat MC (2005) Gene essentiality and the topology of protein interaction networks. Proc R Soc Lond [Biol] 272: 1721–1725CrossRefGoogle Scholar
  101. 101.
    Jeong H, Mason SP, Barabasi AL, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411: 41–42PubMedCrossRefGoogle Scholar
  102. 102.
    Ma HW, Zeng AP (2003) The connectivity structure, giant strong component and centrality of metabolic networks. Bioinformatics 19: 1423–1430PubMedCrossRefGoogle Scholar
  103. 103.
    Dokholyan NV (2005) The architecture of the protein domain universe. Gene 347: 199–206PubMedCrossRefGoogle Scholar
  104. 104.
    Wachi S, Yoneda K, Wu R (2005) Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics 21: 4205–4208PubMedCrossRefGoogle Scholar
  105. 105.
    Tieri P, Valensin S, Latora V, Castellani GC, Marchiori M, Remondini D, Franceschi C (2005) Quantifying the relevance of different mediators in the human immune cell network. Bioinformatics 21: 1639–1643PubMedCrossRefGoogle Scholar
  106. 106.
    Hahn MW, Kern AD (2005) Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol 22: 803–806PubMedCrossRefGoogle Scholar
  107. 107.
    Goh KI, Oh E, Jeong H, Kahng B, Kim D (2002) Classification of scale-free networks. Proc Natl Acad Sci USA 99: 12583–12588PubMedCrossRefGoogle Scholar
  108. 108.
    Soffer SN, Vázquez A (2005) Network clustering coefficient without degree-correlation biases. Physical Review E 71: 057101CrossRefGoogle Scholar
  109. 109.
    Albert R (2005) Scale-free networks in cell biology. J Cell Sci 118: 4947–4957PubMedCrossRefGoogle Scholar
  110. 110.
    Albert R, Barabasi A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74: 47–97CrossRefGoogle Scholar
  111. 111.
    Wagner A, Fell DA (2001) The small world inside large metabolic networks. Proc R Soc Lond [Biol] 268: 1803–1810CrossRefGoogle Scholar
  112. 112.
    Yook SH, Oltvai ZN, Barabási AL (2004) Functional and topological characterization of protein interaction networks. Proteomics 4: 928–942PubMedCrossRefGoogle Scholar
  113. 113.
    Carter SL, Brechbuhler CM, Griffin M, Bond AT (2004) Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 20: 2242–2250PubMedCrossRefGoogle Scholar
  114. 114.
    Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297: 1551–1555PubMedCrossRefGoogle Scholar
  115. 115.
    Wei FP, Meng M, Li S, Ma HR (2006) Comparing two evolutionary mechanisms of modern tRNAs. Mol Phylogenet Evol 38: 1–11PubMedCrossRefGoogle Scholar
  116. 116.
    Dartnell L, Simeonidis E, Hubank M, Tsoka S, Bogle IDL, Papageorgiou LG (2005) Robustness of the p53 network and biological hackers. FEBS Letters 579: 3037–3042PubMedCrossRefGoogle Scholar
  117. 117.
    Said MR, Begley TJ, Oppenheim AV, Lauffenburger DA, Samson LD (2004) Global network analysis of phenotypic effects: protein networks and toxicity modulation in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 101: 18006–18011PubMedCrossRefGoogle Scholar
  118. 118.
    Voit E (2000) Computational Analysis of Biochemical Systems. Cambridge University Press, CambridgeGoogle Scholar
  119. 119.
    Ao P (2005) Metabolic network modelling: Including stochastic effects. Computers & Chem Eng 29: 2297–2303CrossRefGoogle Scholar
  120. 120.
    Holstege FC, Jennings EG, Wyrick JJ, Lee TI, Hengartner CJ, Green MR, Golub TR, Lander ES, Young R (1998) Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95: 717–728PubMedCrossRefGoogle Scholar
  121. 121.
    Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM (1999) Systematic determination of genetic network architecture. Nat Genet 22: 281–285PubMedCrossRefGoogle Scholar
  122. 122.
    D’haeseleer P, Liang S, Somogyi R (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16: 707–726CrossRefGoogle Scholar
  123. 123.
    Wagner A (2001) How to reconstruct a large genetic network from n gene perturbations in fewer than n 2 easy steps. Bioinformatics 17: 1183–1197PubMedCrossRefGoogle Scholar
  124. 124.
    Guthke R, Moller U, Hoffmann M, Thies F, Topfer S (2005) Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. Bioinformatics 21: 1626–1634PubMedCrossRefGoogle Scholar
  125. 125.
    Cavelier G, Anastassiou D (2005) Phenotype analysis using network motifs derived from changes in regulatory network dynamics. Proteins 60: 525–546PubMedCrossRefGoogle Scholar
  126. 126.
    Luscombe NM, Babu MM, Yu HY, Snyder M, Teichmann SA, Gerstein M (2004) Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431: 308–312PubMedCrossRefGoogle Scholar
  127. 127.
    Vilar JMG, Guet CC, Leibler S (2003) Modeling network dynamics: the lac operon, a case study. J Cell Biol 161: 471–476PubMedCrossRefGoogle Scholar
  128. 128.
    Tegner J, Yeung MKS, Hasty J, Collins JJ (2003) Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA 100: 5944–5949PubMedCrossRefGoogle Scholar
  129. 129.
    Wahde M, Hertz J (2000) Coarse-grained reverse engineering of genetic regulatory networks. Biosystems 55: 129–136PubMedCrossRefGoogle Scholar
  130. 130.
    Arkin A, Shen P, Ross J (1997) A test case of correlation metric construction of a reaction pathway from measurements. Science 277: 1275–1279CrossRefGoogle Scholar
  131. 131.
    Remondini D, O’Connell B, Intrator N, Sedivy JM, Neretti N, Castellani GC, Cooper LN (2005) Targeting c-Myc-activated genes with a correlation method: Detection of global changes in large gene expression network dynamics. Proc Natl Acad Sci USA 102: 6902–6906PubMedCrossRefGoogle Scholar
  132. 132.
    Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7: 601–620PubMedCrossRefGoogle Scholar
  133. 133.
    Tamada Y, Kim S, Bannai H, Imoto S, Tashiro K, Kuhara S, Miyano S (2003) Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics 19: II227–II236PubMedCrossRefGoogle Scholar
  134. 134.
    Tchuraev RN, Galimzyanov AV (2001) Modeling of actual eukaryotic control gene sub-networks based on the method of generalized threshold models. Mol Biol 35: 933–939CrossRefGoogle Scholar
  135. 135.
    Espinosa-soto C, Padilla-Longoria P, Alvarez-Buylla ER (2004) A gene regulatory network model for cell-fate determination during Arabidopsis thalianal flower development that is robust and recovers experimental gene expression profiles. Plant Cell 16: 2923–2939PubMedCrossRefGoogle Scholar
  136. 136.
    Shmulevich I, Dougherty ER, Kim S, Zhang W (2002) Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18: 261–274PubMedCrossRefGoogle Scholar
  137. 137.
    Ramo P, Kesseli J, Yli-Harja O (2005) Stability of functions in Boolean models of gene regulatory networks. Chaos 15: 34101PubMedCrossRefGoogle Scholar
  138. 138.
    Shmulevich I, Kauffman SA, Aldana M (2005) Eukaryotic cells are dynamically ordered or critical but not chaotic. Proc Natl Acad Sci USA 102: 13439–13444PubMedCrossRefGoogle Scholar
  139. 139.
    Kam Z (2002) Generalized analysis of experimental data for interrelated biological measurements. Bull Math Biol 64: 133–145PubMedCrossRefGoogle Scholar
  140. 140.
    Du P, Gong H, Wurtele ES, Dickerson JA (2005) Modeling gene expression networks using fuzzy logic. IEEE T Syst Man Cy B 35: 1351–1359CrossRefGoogle Scholar
  141. 141.
    Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308: 523–529PubMedCrossRefGoogle Scholar
  142. 142.
    Xing B, van der Laan MJ (2005) A causal inference approach for constructing transcriptional regulatory networks. Bioinformatics 21: 4007–4013PubMedCrossRefGoogle Scholar
  143. 143.
    Kelley BP, Yuan BB, Lewitter F, Sharan R, Stockwell BR, Ideker T (2004) PathBLAST: a tool for alignment of protein interaction networks. Nucleic Acids Res 32: W83–W88PubMedCrossRefGoogle Scholar
  144. 144.
    Forst CV, Schulten K (1999) Evolution of metabolisms: a new method for the comparison of metabolic pathways using genomics information. J Comput Biol 6: 343–360PubMedCrossRefGoogle Scholar
  145. 145.
    Ogata H, Fujibuchi W, Goto S, Kanehisa M (2000) A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Nucleic Acids Res 28: 4021–4028PubMedCrossRefGoogle Scholar
  146. 146.
    Dandekar T, Schuster S, Snel B, Huynen M, Bork P (1999) Pathway alignment: application to the comparative analysis of glycolytic enzymes. Biochem J 343: 115–124PubMedCrossRefGoogle Scholar
  147. 147.
    von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P (2002) Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417: 399–403CrossRefGoogle Scholar
  148. 148.
    Ideker T, Lauffenburger DA (2003) Building with a scaffold: emerging strategies for high-to low-level cellular modeling. Trends Biotechnol 21: 255–262PubMedCrossRefGoogle Scholar
  149. 149.
    Kelley BP, Sharan R, Karp RM, Sittler T, Root DE, Stockwell BR, Ideker T (2003) Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc Natl Acad Sci USA 100: 11394–11399PubMedCrossRefGoogle Scholar
  150. 150.
    Sharan R, Ideker T, Kelley B, Shamir R, Karp RM (2005) Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. J Comput Biol 12: 835–846PubMedCrossRefGoogle Scholar
  151. 151.
    Sharan R, Suthram S, Kelley RM, Kuhn T, McCuine S, Uetz P, Sittler T, Karp RM, Ideker T (2005) Conserved patterns of protein interaction in multiple species. Proc Natl Acad Sci USA 102: 1974–1979PubMedCrossRefGoogle Scholar
  152. 152.
    Choy C, Jansson J, Sadakane K, Sung WK (2005) Computing the maximum agreement of phylogenetic networks. Theor Comput Sci 335: 93–107CrossRefGoogle Scholar
  153. 153.
    Palla G, Derenyi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435: 814–818PubMedCrossRefGoogle Scholar
  154. 154.
    Clipsham R, Zhang YH, Huang BL, McCabe ERB (2002) Genetic network identification by high density, multiplexed reversed transcriptional (HD-MRT) analysis in steroidogenic axis model cell lines. Mol Genet Metab 77: 159–178PubMedCrossRefGoogle Scholar
  155. 155.
    Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99: 7821–7826PubMedCrossRefGoogle Scholar
  156. 156.
    Barabasi AL, de Menezes MA, Balensiefer S, Brockman J (2004) Hot spots and universality in network dynamics. Eur Physical J B 38: 169–175CrossRefGoogle Scholar
  157. 157.
    Batagelj V, Mrvar A (2003) Pajek — Analysis and visualization of large networks. In: M Jünger, P Mutzel (eds): Graph Drawing Software. Springer, Berlin, 77–103Google Scholar
  158. 158.
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 13: 2498–2504PubMedCrossRefGoogle Scholar
  159. 159.
    Junker BH, Klukas C, Schreiber F (2006) VANTED: A system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics 7: 109PubMedCrossRefGoogle Scholar
  160. 160.
    Hu Z, Mellor J, Wu J, Yamada T, Holloway D, DeLisi C (2005) VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res 33: W352–W357PubMedCrossRefGoogle Scholar
  161. 161.
    Maere S, Heymans K, Kuiper M (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21: 3448–3449PubMedCrossRefGoogle Scholar
  162. 162.
    Koschützki D, Lehmann KA, Peeters L, Richter S, Tenfelde-Podehl D, Zlotowski O (2005) Centrality Indices. In: U Brandes, T Erlebach (eds): Network Analysis. LNCS Tutorial 3418. Springer, 16–61Google Scholar
  163. 163.
    Yu HY, Zhu XW, Greenbaum D, Karro J, Gerstein M (2004) TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics. Nucleic Acids Res 32: 328–337PubMedCrossRefGoogle Scholar
  164. 164.
    Ludemann A, Weicht D, Selbig J, Kopka J (2004) PaVESy: Pathway visualization and editing system. Bioinformatics 20: 2841–2844PubMedCrossRefGoogle Scholar
  165. 165.
    Toyoda T, Konagaya A (2003) KnowledgeEditor: a new tool for interactive modeling and analyzing biological pathways based on microarray data. Bioinformatics 19: 433–43PubMedCrossRefGoogle Scholar
  166. 166.
    Breitkreutz BJ, Stark C, Tyers M (2003) The GRID: the General Repository for Interaction Datasets. Genome Biol 4: R23PubMedCrossRefGoogle Scholar
  167. 167.
    Breitkreutz BJ, Stark C, Tyers M (2003) Osprey: A network visualization system. Genome Biol 4: R22PubMedCrossRefGoogle Scholar
  168. 168.
    Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M (2004) The KEGG resources for deciphering the genome. Nucleic Acids Res 32: D277–D280PubMedCrossRefGoogle Scholar
  169. 169.
    Hwang D, Rust AG, Ramsey S, Smith JJ, Leslie DM, Weston AD, Atauri PD, Aitchison JD, Hood L, Siegel AF et al. (2005) A data integration methodology for systems biology. Proc Natl Acad Sci USA 102: 17296–17301PubMedCrossRefGoogle Scholar

Copyright information

© Birkhäuser Verlag/Switzerland 2007

Authors and Affiliations

  • Victoria J. Nikiforova
    • 1
  • Lothar Willmitzer
    • 1
  1. 1.Max-Planck-Institut für Molekulare PflanzenphysiologiePotsdam-GolmGermany

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