Skip to main content

Biomolecular Network Structure and Function

  • Reference work entry
Encyclopedia of Complexity and Systems Science

Definition of the Subject

Biological research over the past century or so has been dominated by reductionism – identifying and characterizing individualbiomolecules – and has enjoyed enormous success. Throughout this history, however, it has become increasingly clear that an individualbiomolecule can rarely account for a discrete biological function on its own. A biological process is almost always the result ofa complex interplay of relationships amongst biomolecules [5,19,50,51,67,68,90,128], and thetreatment of these relationships as a graph is a natural and useful abstraction.

Broadly speaking, biomolecular networkis a graph representation of relationships (of which there aremany types) amongst a group of biomolecules. Vertices or nodes represent biomolecules, including macromolecules such as genes, proteins, and RNAs, orsmall biomolecules like amino acids, sugars, and nucleic acids. In the next few sections, we focus mostly on the...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 3,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

Biomolecule:

Any organic molecule that is produced by or essential to a living organism, sometimes specifically referring to macromolecules such as a protein or nucleic acid.

Biomolecular network :

a graph representation of relationships amongst a group of biomolecules. Nodes or vertices represent biomolecules. An edge or link between two vertices indicates a relationship between the corresponding biomolecules, for example, physical interaction, genetic interaction, or regulatory relationship.

Protein‐protein interaction:

the physical association of two protein molecules with each other. A pair of proteins can interact directly with physical contact, or indirectly through other biomolecule(s), often other protein(s).

Yeast two‐hybrid:

an experimental method to examine protein‐protein interaction, in which one protein is fused to a transcriptional activation domain (the GAL4 activation domain) and the other to a DNA‐binding domain (the GAL4 DNA‐binding domain), and both fusion proteins are introduced into yeast. Expression of a GAL4‐regulated reporter gene with the appropriate DNA‐binding sites upstream of its promoter indicates that the two proteins physically interact.

Genetic interaction (epistasis):

functional interaction between genes, in which the action of one gene is modified by the other gene, sometimes called the modifier gene. The gene whose phenotype is expressed is said to be epistatic, while the one whose phenotype is altered or suppressed is said to be hypostatic. Epistasis can either refer to this phenomenon, or more broadly to any case in which two mutations together cause a phenotype that is surprising given the phenotypes of each single mutation alone.

“Single‐color” network:

a network with edges defined by only one type of interaction or relationship.

“Multi‐color” network:

a network with edges defined by more than one type of interaction or relationship, with each type corresponding to a different ‘color’.

Scale‐free network :

See Power‐law network

Power‐law network :

a network defined by a degree distribution which follows \( { P(k)\sim k^{-\gamma} } \), where the probability P(k) that a vertex in the network connects with k other vertices is roughly proportional to \( { k^{-\gamma} } \). Sometimes networks that exhibit this behavior only at high degree are also called power‐law. The coefficient γ seems to vary approximately between 2 and 3 for most real networks. In a power‐law network, majority of the vertices have low degree (connectivity), while a small fraction of the vertices have very high degree. Highly‐connected vertices are referred to as hubs.

Small‐world network :

a special type of network with: 1) short characteristic path length, such that most vertex pairs are connected to one another via only a small number of edges) and 2) high clustering coefficient, such that neighbors of a given vertex tend to be connected to one another.

Network motif :

a specific pattern of connected vertices and edges that occurs frequently within a given network.

Bibliography

Primary Literature

  1. Agrawal H (2002) Extreme self‐organization in networks constructed from gene expression data. Phys Rev Lett 89:268702–268702

    ADS  Google Scholar 

  2. Albert I, Albert R (2004) Conserved network motifs allow protein‐protein interaction prediction. Bioinformatics20(18):3346–3352

    Google Scholar 

  3. AlbertR, Barabasi AL (2002) Statistical mechanics ofcomplex networks. Rev Mod Phys 74(47)

    MathSciNet  ADS  MATH  Google Scholar 

  4. Albert R, Jeong H et al (2000) Error and attack tolerance of complex networks. Nature 406:378–382

    ADS  Google Scholar 

  5. Alon U (2003) Biological networks: the tinkerer as an engineer. Science 301:1866–1867

    ADS  Google Scholar 

  6. Amaral LA, Scala A et al (2000) Classes of small-world networks. Proc Natl Acad Sci USA 97(21):11149–52

    ADS  Google Scholar 

  7. Asthana S, King OD et al (2004) Predicting protein complex membership using probabilistic network reliability. Genome Res14(6):1170–5

    Google Scholar 

  8. AveryL, Wasserman S (1992) Ordering gene function: theinterpretation of epistasis in regulatory hierarchies. Trends Genet8(9):312–6

    Google Scholar 

  9. Bader GD, Hogue CW (2002) Analyzing yeast protein‐protein interaction data obtained from different sources. Nat Biotechnol 20(10):991–7

    Google Scholar 

  10. Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4(1):2

    Google Scholar 

  11. Bader JS (2003) Greedily building protein networks with confidence. Bioinformatics 19(15):1869–74

    Google Scholar 

  12. Bader JS, Chaudhuri A et al (2004) Gaining confidence in high‐throughput protein interaction networks. NatBiotechnol 22(1):78–85

    Google Scholar 

  13. BalazsiG, Barabasi AL et al (2005) Topological units ofenvironmental signal processing in the transcriptional regulatorynetwork of Escherichia coli. Proc Natl Acad Sci USA 102(22):7841–6

    ADS  Google Scholar 

  14. Bar‐Joseph Z (2003) Computational discovery of gene modules and regulatory networks. Nature Biotechnol 21:1337–1342

    Google Scholar 

  15. Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–12

    MathSciNet  ADS  Google Scholar 

  16. Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet 5(2):101–13

    Google Scholar 

  17. Bornholdt S, Ebel H (2001) World Wide Web scaling exponent from Simon's 1955 model. Phys Rev E 64(3):35104

    ADS  Google Scholar 

  18. BornholdtS, Schuster HG (2003) Handbook of Graphs andNetworks: from the Genome to the Internet

    Google Scholar 

  19. Bray D (2003) Molecular networks: the top-down view. Science 301:1864–1865

    ADS  Google Scholar 

  20. Broder A (2000) Graph structure in the web. Comput Netw 33:309–320

    Google Scholar 

  21. Callaway DS, Newman MEJ et al (2000) Network robustness and fragility: percolation on random graphs. Phys Rev Lett 85:5468–5471

    ADS  Google Scholar 

  22. Cho RJ, Campbell MJ et al (1998) A genome‐wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2(1):65–73

    Google Scholar 

  23. Cohen R, Erez K et al (2000) Resilience of the Internet to random breakdowns. Phys Rev Lett 85:4626–4628

    ADS  Google Scholar 

  24. deLichtenberg U, Jensen LJ et al (2005) Dynamic complexformation during the yeast cell cycle. Science 307(5710):724–7

    ADS  Google Scholar 

  25. Diestel R (2005) Graph Theory, 3rd edn. Springer, Heidelberg

    MATH  Google Scholar 

  26. Dobrin R, Beg QK et al (2004) Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMCBioinformatics 5(1):10

    Google Scholar 

  27. DorogovtsevSN, Mendes JF (2003). Evolution of Networks:from Biological Nets to the Internet and WWW. Oxford University Press

    Google Scholar 

  28. Drees BL, Thorsson V et al (2005) Derivation of genetic interaction networks from quantitative phenotype data. Genome Biol 6(4):R38

    Google Scholar 

  29. Fanning AS, Anderson JM (1996) Protein‐protein interactions: PDZ domain networks. Curr Biol6(11):1385–8

    Google Scholar 

  30. FarhKK, Grimson A et al (2005) The widespread impact ofmammalian MicroRNAs on mRNA repression and evolution. Science310(5755):1817–21

    ADS  Google Scholar 

  31. FarkasIJ, Wu C et al (2006) Topological basis of signalintegration in the transcriptional‐regulatory network of the yeast,Saccharomyces cerevisiae. BMC Bioinformatics 7:478

    Google Scholar 

  32. Featherstone DE, Broadie K (2002) Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network. Bioessays 24:267–274

    Google Scholar 

  33. Fell DA, Wagner A (2000) The small world of metabolism. Nat Biotechnol 18(11):1121–2

    Google Scholar 

  34. Freudenberg J, Zimmer R et al (2002) A hypergraph‐based method for unification of existing protein structure- and sequence‐families. In Silico Biol 2(3):339–49

    Google Scholar 

  35. Gavin AC, Bosche M et al (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature415(6868):141–7

    ADS  Google Scholar 

  36. GeH, Liu Z et al (2001) Correlation between transcriptome andinteractome mapping data from Saccharomyces cerevisiae. Nat Genet29(4):482–6

    MathSciNet  Google Scholar 

  37. GeisslerS, Siegers K et al (1998) A novel protein complexpromoting formation of functional alpha‐ and gamma‐tubulin. Embo J17(4):952–66

    Google Scholar 

  38. Getoor L, Rhee JT et al (2004) Understanding tuberculosis epidemiology using structured statistical models. Artif Intell Med 30(3):233–56

    Google Scholar 

  39. Giaever G (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature 418:387–391

    ADS  Google Scholar 

  40. Gietz RD, B Triggs‐Raine et al (1997) Identification of proteins that interact with a protein of interest: applications of the yeast two‐hybrid system. Mol Cell Biochem 172(1–2):67–79

    Google Scholar 

  41. Giot L (2003) A protein interaction map of Drosophila melanogaster. Science 302:1727–1736

    ADS  Google Scholar 

  42. Girvan M, Newman ME (2002) J Community structure in social and biological networks. Proc Natl Acad Sci USA 99:7821–7826

    MathSciNet  ADS  MATH  Google Scholar 

  43. Goldberg DS, Roth FP (2003) Assessing experimentally derived interactions in a small world. Proc Natl Acad Sci USA 3:3

    MathSciNet  Google Scholar 

  44. Guelzim N, Bottani S et al (2002) Topological and causal structure of the yeast transcriptional regulatory network. Nat Genet31(1):60–3

    Google Scholar 

  45. GunsalusK C, Ge H et al (2005) Predictive models of molecularmachines involved in Caenorhabditis elegans early embryogenesis.Nature 436(7052):861–5

    ADS  Google Scholar 

  46. Han JD, Bertin N et al (2004) Evidence for dynamically organized modularity in the yeast protein‐protein interaction network. Nature 430(6995):88–93

    ADS  Google Scholar 

  47. Han JD, Dupuy D et al (2005) Effect of sampling on topology predictions of protein‐protein interaction networks. Nat Biotechnol23(7):839–44

    Google Scholar 

  48. HaneinD, Matlack KE et al (1996) Oligomeric rings of theSec61p complex induced by ligands required for protein translocation.Cell 87(4):721–32

    Google Scholar 

  49. Harbison CT, Gordon DB et al (2004) Transcriptional regulatory code of a eukaryotic genome. Nature 431(7004):99–104

    ADS  Google Scholar 

  50. Hartwell LH, Hopfield JJ et al (1999) From molecular to modular cell biology. Nature 402:C47–C52

    Google Scholar 

  51. Hasty J, McMillen D et al (2002) Engineered gene circuits. Nature 420:224–230

    ADS  Google Scholar 

  52. Ho Y, Gruhler A et al (2002) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415(6868):180–3

    ADS  Google Scholar 

  53. Holme P, Huss M et al (2003) Subnetwork hierarchies of biochemical pathways. Bioinformatics19:532–538

    Google Scholar 

  54. HuhWK, Falvo JV et al (2003) Global analysis of proteinlocalization in budding yeast. Nature 425(6959):686–91

    ADS  Google Scholar 

  55. Ihmels J (2002) Revealing modular organization in the yeast transcriptional network. Nat Genet31:370–377

    Google Scholar 

  56. ItoT (2001) A comprehensive two‐hybrid analysis to explore theyeast protein interactome. Proc Natl Acad Sci USA 98:4569–4574

    ADS  Google Scholar 

  57. Ito T, Tashiro K et al (2000) Toward a protein‐protein interaction map of the budding yeast: A comprehensive system to examine two‐hybrid interactions in all possible combinations between the yeast proteins. Proc Natl Acad Sci USA 97(3):1143–7

    ADS  Google Scholar 

  58. Jacob F, Monod J (1961) Genetic regulatory mechanisms in the synthesis of proteins. J Mol Biol3:318–56

    Google Scholar 

  59. Jansen R (2003) A Bayesian networks approach for predicting protein‐protein interactions from genomic data. Science 302:449–453

    MathSciNet  ADS  Google Scholar 

  60. JansenR, Greenbaum D et al (2002) Relating whole‐genomeexpression data with protein‐protein interactions. Genome Res 12(1):37–46

    Google Scholar 

  61. Jansen R, Lan N et al (2002) Integration of genomic datasets to predict protein complexes in yeast. J Struct Funct Genomics 2:71–81

    Google Scholar 

  62. Jansen R, Yu H et al (2003) A Bayesian networks approach for predicting protein‐protein interactions from genomic data. Science 302(5644):449–53

    ADS  Google Scholar 

  63. Jeong H, Mason SP et al (2001) Lethality and centrality in protein networks. Nature 411(6833):41–2

    ADS  Google Scholar 

  64. Jeong H, Tombor B et al (2000) The large-scale organization of metabolic networks. Nature407:651–654

    ADS  Google Scholar 

  65. JuvanP, Demsar J et al (2005) GenePath: from mutations togenetic networks and back. Nucleic Acids Res 33(Web Server issue):W749–52

    Google Scholar 

  66. King OD (2004) Comment on Subgraphs in random networks. Phys Rev E Stat Nonlin Soft Matter Phys 70(5 Pt 2):058101. author reply 058102

    ADS  Google Scholar 

  67. Kitano H (2002) Computational systems biology. Nature 420:206–210

    ADS  Google Scholar 

  68. Koonin EV, Wolf YI et al (2002) The structure of the protein universe and genome evolution. Nature 420:218–223

    ADS  Google Scholar 

  69. Krogan NJ, Cagney G et al (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature440(7084):637–43

    ADS  Google Scholar 

  70. KumarA, Agarwal S et al (2002) Subcellular localization of theyeast proteome. Genes Dev 16(6):707–19

    Google Scholar 

  71. Launer RL, Wilkinson GN (1979) Robustness in statistics. Academic Press, New York

    MATH  Google Scholar 

  72. Lee I, Date SV et al (2004) A probabilistic functional network of yeast genes. Science 306(5701):1555–8

    ADS  Google Scholar 

  73. Lee TI, Rinaldi NJ et al (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298(5594):799–804

    ADS  Google Scholar 

  74. Li S (2004) A map of the interactome network of the metazoan, C elegans. Science 303:590–593

    Google Scholar 

  75. Li W, Liu Y et al (2007) Dynamical systems for discovering protein complexes and functional modules from biological networks. IEEE/ACM Trans Comput Biol Bioinform 4(2):233–50

    Google Scholar 

  76. Lockhart DJ, Dong H et al (1996) Expression monitoring by hybridization to high‐density oligonucleotide arrays. Nat Biotechnol 14(13):1675–80

    Google Scholar 

  77. Ma HW, Buer J et al (2004) Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach. BMC Bioinformatics 5:199

    Google Scholar 

  78. MacIsaac KD, Wang T et al (2006) An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinformatics 7:113

    Google Scholar 

  79. Mangan S, Itzkovitz S et al (2006) The incoherent feed‐forward loop accelerates the response‐time of the gal system of Escherichia coli. J Mol Biol 356(5):1073–81

    Google Scholar 

  80. Maslov S, Sneppen K (2002) Specificity and stability in topology of protein networks. Science296:910–913

    ADS  Google Scholar 

  81. Ma'ayanA, Jenkins SL et al (2005) Formation of regulatorypatterns during signal propagation in a Mammalian cellular network.Science 309(5737):1078–83

    ADS  Google Scholar 

  82. Milo R, Itzkovitz S et al (2004) Superfamilies of evolved and designed networks. Science 303(5663):1538–42

    ADS  Google Scholar 

  83. Milo R, S Shen-Orr et al (2002) Network motifs: simple building blocks of complex networks. Science298(5594):824–7

    ADS  Google Scholar 

  84. MonodJ, Cohen‐Bazire G et al (1951) The biosynthesis ofbeta‐galactosidase (lactase) in Escherichia coli; the specificity ofinduction. Biochim Biophys Acta 7(4):585–99

    Google Scholar 

  85. Monod J, Jacob F (1961) Teleonomic mechanisms in cellular metabolism, growth, and differentiation. Cold Spring Harb Symp Quant Biol 26:389–401

    Google Scholar 

  86. Nadvornik P, Drozen V (1964) Models of Neurons and Neuron Networks. Act Nerv Super (Praha) 6:293–302

    Google Scholar 

  87. Newman MEJ (2002) Assortative mixing in networks. Phys RevLett 89:208701–208701

    ADS  Google Scholar 

  88. Newman ME, Strogatz SH et al (2001) Random graphs with arbitrary degree distributions and their applications. Phys Rev E Stat Nonlin Soft Matter Phys 64(2 Pt 2):026118

    ADS  Google Scholar 

  89. Novick A, Weiner M (1957) Enzyme Induction as an All-or-None Phenomenon. Proc Natl Acad Sci USA 43(7):553–66

    ADS  Google Scholar 

  90. Oltvai ZN, Barabasi AL (2002) Life's complexity pyramid. Science 298:763–764

    Google Scholar 

  91. Pastor‐SatorrasR, Vazquez A et al (2001) Dynamical andcorrelation properties of theInternet.Phys. Rev. Lett. 87:258701–258701

    Google Scholar 

  92. PtacekJ, Devgan G et al (2005) Global analysis of proteinphosphorylation in yeast. Nature 438(7068):679–84

    ADS  Google Scholar 

  93. Qi Y, Klein‐Seetharaman J et al (2005) Random forest similarity for protein‐protein interaction prediction from multiple sources. Pac Symp Biocomput:531–42

    Google Scholar 

  94. Rajewsky N (2006) microRNA target predictions in animals. NatGenet 38 Suppl:S8–13

    Google Scholar 

  95. Ravasz E, Barabasi AL (2003) Hierarchical organization in complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 67:026112–026112

    ADS  Google Scholar 

  96. Ravasz E, Somera AL et al (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551–5

    ADS  Google Scholar 

  97. Rives AW, Galitski T (2003) Modular organization of cellular networks. Proc Natl Acad Sci USA 100(3):1128–33

    ADS  Google Scholar 

  98. Rouvray H (1990) The Origins of Chemical Graph Theory. In: Bonchev D, Rouvray DH (eds) Chemical Graph Theory: Introduction and Fundamentals, vol 41. Gordon and Breach Science Publishers, New York

    Google Scholar 

  99. Rual JF, Venkatesan K et al (2005) Towards a proteome‐scale map of the human protein‐protein interaction network. Nature 437(7062):1173–8

    ADS  Google Scholar 

  100. Schena M, Shalon D et al (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235):467–470

    ADS  Google Scholar 

  101. Schleif R (2000) Regulation of the L‐arabinose operon of Escherichia coli. Trends Genet 16(12):559–65

    Google Scholar 

  102. Schuster S, Pfeiffer T et al (2002) Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae. Bioinformatics 18:351–361

    Google Scholar 

  103. Shen-Orr SS, Milo R et al (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31(1):64–8

    Google Scholar 

  104. Simon HA (1955) On a class of skew distribution functions. Biometrika 42:425–440

    MathSciNet  MATH  Google Scholar 

  105. Simonis N, Gonze D et al (2006) Modularity of the transcriptional response of protein complexes in yeast. J Mol Biol363(2):589–610

    Google Scholar 

  106. SimonisN, van Helden J et al (2004) Transcriptional regulationof protein complexes in yeast. Genome Biol 5(5):R33

    Google Scholar 

  107. Smith LM, Fung S et al (1985) The synthesis of oligonucleotides containing an aliphatic amino group at the 5' terminus: synthesis of fluorescent DNA primers for use in DNA sequence analysis. Nucleic Acids Res 13(7):2399–412

    Google Scholar 

  108. Smith LM, Sanders JZ et al (1986) Fluorescence detection in automated DNA sequence analysis. Nature 321(6071):674–9

    ADS  Google Scholar 

  109. Snel B, Bork P et al (2002) The identification of functional modules from the genomic association of genes. Proc Natl Acad Sci USA 99:5890–5895

    ADS  Google Scholar 

  110. Sole RV, R Pastor‐Satorras et al (2002) A Model of Large-Scale Proteome Evolution. Adv Complex Syst5:43–54

    Google Scholar 

  111. SoodP, Krek A et al (2006) Cell‐type‐specific signatures ofmicroRNAs on target mRNA expression. Proc Natl Acad Sci USA 103(8):2746–51

    ADS  Google Scholar 

  112. Spellman PT, Sherlock G et al (1998) Comprehensive identification of cell cycle‐regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Cell Biol 9(12):3273–3297

    Google Scholar 

  113. Spirin V, Mirny LA (2003) Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci USA 100(21):12123–8

    ADS  Google Scholar 

  114. St Onge RP, Mani R et al (2007) Systematic pathway analysis using high‐resolution fitness profiling of combinatorial genedeletions. Nat Genet 39(2):199–206

    Google Scholar 

  115. StarkA, Brennecke J et al (2005) Animal MicroRNAs conferrobustness to gene expression and have a significant impact on 3'UTRevolution. Cell 123(6):1133–46

    Google Scholar 

  116. StelzlU, Worm U et al (2005) A human protein‐proteininteraction network: a resource for annotating the proteome. Cell122(6):957–68

    Google Scholar 

  117. Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268–76

    ADS  Google Scholar 

  118. Stuart JM, Segal E et al (2003) A gene‐coexpression network for global discovery of conserved genetic modules. Science 302:249–255

    ADS  Google Scholar 

  119. Tanaka R (2005) Scale-rich metabolic networks. Phys Rev Lett 94(16):168101

    ADS  Google Scholar 

  120. Taylor RJ, Siegel AF et al (2007) Network motif analysis of a multi-mode genetic‐interaction network. Genome Biol 8(8):R160

    Google Scholar 

  121. Thieffry D, Huerta AM et al (1998) From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli. Bioessays 20(5):433–40

    Google Scholar 

  122. Tong AH, Lesage G et al (2004) Global mapping of the yeast genetic interaction network. Science 303(5659):808–13

    ADS  Google Scholar 

  123. Tornow S, Mewes HW (2003) Functional modules by relating protein interaction networks and gene expression. Nucleic Acids Res31:6283–6289

    Google Scholar 

  124. TsangJ, Zhu J et al (2007) MicroRNA‐mediated feedback andfeedforward loops are recurrent network motifs in mammals. Mol Cell26(5):753–67

    Google Scholar 

  125. Uetz P, Giot L et al (2000) A comprehensive analysis of protein‐protein interactions in Saccharomyces cerevisiae. Nature 403(6770):623–7

    ADS  Google Scholar 

  126. Wagner A (2001) The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes. Mol Biol Evol 18(7):1283–92

    Google Scholar 

  127. Wagner A, Fell DA (2001) The small world inside large metabolic networks. Proc Biol Sci 268(1478):1803–10

    Google Scholar 

  128. Wall ME, Hlavacek WS et al (2004) Design of gene circuits: lessons from bacteria. Nature Rev Genet 5:34–42

    Google Scholar 

  129. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small‐world’ networks. Nature 393(6684):440–2

    ADS  Google Scholar 

  130. Wen X, Fuhrman S et al (1998) Large-scale temporal gene expression mapping of central nervous system development. Proc Natl Acad Sci USA 95(1):334–339

    ADS  Google Scholar 

  131. Winzeler EA (1999) Functional characterization of the S cerevisiae genome by gene deletion and parallel analysis. Science 285:901–906

    Google Scholar 

  132. Wong SL, Zhang LV et al (2004) Combining biological networks to predict genetic interactions. Proc Natl Acad Sci USA 101(44):15682–7

    ADS  Google Scholar 

  133. Wunderlich Z, Mirny LA (2006) Using the Topology of Metabolic Networks to Predict Viability of Mutant Strains. Biophys J 91(6):2304–2311

    Google Scholar 

  134. Yeger-Lotem E, Sattath S et al (2004) Network motifs in integrated cellular networks of transcription‐regulation and protein‐protein interaction. Proc Natl Acad Sci USA 101(16):5934–9

    ADS  Google Scholar 

  135. Yook SH, Oltvai ZN et al (2004) Functional and topological characterization of protein interaction networks. Proteomics 4(4):928–42

    Google Scholar 

  136. Yu H, Gerstein M (2006) Genomic analysis of the hierarchical structure of regulatory networks. Proc Natl Acad Sci USA 103(40):14724–31

    ADS  Google Scholar 

  137. Yu H, Luscombe NM et al (2003) Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet 19(8):422–7

    Google Scholar 

  138. Zhang L, King O et al (2005) Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network. J Biol 4(2):6

    Google Scholar 

  139. Zhang LV, Wong SL et al (2004) Predicting co‐complexed protein pairs using genomic and proteomic data integration. BMC Bioinformatics 5(1):38

    Google Scholar 

Books and Reviews

  1. Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet 5(2):101–13

    Google Scholar 

  2. Diestel R (2005) Graph Theory, 3rd edn. Springer, Heidelberg

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag

About this entry

Cite this entry

Zhang, L.V., Roth, F.P. (2009). Biomolecular Network Structure and Function. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_38

Download citation

Publish with us

Policies and ethics