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, a 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...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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
Agrawal H (2002) Extreme self‐organization in networks constructed from gene expression data. Phys Rev Lett 89:268702–268702
Albert I, Albert R (2004) Conserved network motifs allow protein‐protein interaction prediction. Bioinformatics20(18):3346–3352
AlbertR, Barabasi AL (2002) Statistical mechanics ofcomplex networks. Rev Mod Phys 74(47)
Albert R, Jeong H et al (2000) Error and attack tolerance of complex networks. Nature 406:378–382
Alon U (2003) Biological networks: the tinkerer as an engineer. Science 301:1866–1867
Amaral LA, Scala A et al (2000) Classes of small-world networks. Proc Natl Acad Sci USA 97(21):11149–52
Asthana S, King OD et al (2004) Predicting protein complex membership using probabilistic network reliability. Genome Res14(6):1170–5
AveryL, Wasserman S (1992) Ordering gene function: theinterpretation of epistasis in regulatory hierarchies. Trends Genet8(9):312–6
Bader GD, Hogue CW (2002) Analyzing yeast protein‐protein interaction data obtained from different sources. Nat Biotechnol 20(10):991–7
Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4(1):2
Bader JS (2003) Greedily building protein networks with confidence. Bioinformatics 19(15):1869–74
Bader JS, Chaudhuri A et al (2004) Gaining confidence in high‐throughput protein interaction networks. NatBiotechnol 22(1):78–85
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
Bar‐Joseph Z (2003) Computational discovery of gene modules and regulatory networks. Nature Biotechnol 21:1337–1342
Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–12
Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet 5(2):101–13
Bornholdt S, Ebel H (2001) World Wide Web scaling exponent from Simon's 1955 model. Phys Rev E 64(3):35104
BornholdtS, Schuster HG (2003) Handbook of Graphs andNetworks: from the Genome to the Internet
Bray D (2003) Molecular networks: the top-down view. Science 301:1864–1865
Broder A (2000) Graph structure in the web. Comput Netw 33:309–320
Callaway DS, Newman MEJ et al (2000) Network robustness and fragility: percolation on random graphs. Phys Rev Lett 85:5468–5471
Cho RJ, Campbell MJ et al (1998) A genome‐wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2(1):65–73
Cohen R, Erez K et al (2000) Resilience of the Internet to random breakdowns. Phys Rev Lett 85:4626–4628
deLichtenberg U, Jensen LJ et al (2005) Dynamic complexformation during the yeast cell cycle. Science 307(5710):724–7
Diestel R (2005) Graph Theory, 3rd edn. Springer, Heidelberg
Dobrin R, Beg QK et al (2004) Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMCBioinformatics 5(1):10
DorogovtsevSN, Mendes JF (2003). Evolution of Networks:from Biological Nets to the Internet and WWW. Oxford University Press
Drees BL, Thorsson V et al (2005) Derivation of genetic interaction networks from quantitative phenotype data. Genome Biol 6(4):R38
Fanning AS, Anderson JM (1996) Protein‐protein interactions: PDZ domain networks. Curr Biol6(11):1385–8
FarhKK, Grimson A et al (2005) The widespread impact ofmammalian MicroRNAs on mRNA repression and evolution. Science310(5755):1817–21
FarkasIJ, Wu C et al (2006) Topological basis of signalintegration in the transcriptional‐regulatory network of the yeast,Saccharomyces cerevisiae. BMC Bioinformatics 7:478
Featherstone DE, Broadie K (2002) Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network. Bioessays 24:267–274
Fell DA, Wagner A (2000) The small world of metabolism. Nat Biotechnol 18(11):1121–2
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
Gavin AC, Bosche M et al (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature415(6868):141–7
GeH, Liu Z et al (2001) Correlation between transcriptome andinteractome mapping data from Saccharomyces cerevisiae. Nat Genet29(4):482–6
GeisslerS, Siegers K et al (1998) A novel protein complexpromoting formation of functional alpha‐ and gamma‐tubulin. Embo J17(4):952–66
Getoor L, Rhee JT et al (2004) Understanding tuberculosis epidemiology using structured statistical models. Artif Intell Med 30(3):233–56
Giaever G (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature 418:387–391
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
Giot L (2003) A protein interaction map of Drosophila melanogaster. Science 302:1727–1736
Girvan M, Newman ME (2002) J Community structure in social and biological networks. Proc Natl Acad Sci USA 99:7821–7826
Goldberg DS, Roth FP (2003) Assessing experimentally derived interactions in a small world. Proc Natl Acad Sci USA 3:3
Guelzim N, Bottani S et al (2002) Topological and causal structure of the yeast transcriptional regulatory network. Nat Genet31(1):60–3
GunsalusK C, Ge H et al (2005) Predictive models of molecularmachines involved in Caenorhabditis elegans early embryogenesis.Nature 436(7052):861–5
Han JD, Bertin N et al (2004) Evidence for dynamically organized modularity in the yeast protein‐protein interaction network. Nature 430(6995):88–93
Han JD, Dupuy D et al (2005) Effect of sampling on topology predictions of protein‐protein interaction networks. Nat Biotechnol23(7):839–44
HaneinD, Matlack KE et al (1996) Oligomeric rings of theSec61p complex induced by ligands required for protein translocation.Cell 87(4):721–32
Harbison CT, Gordon DB et al (2004) Transcriptional regulatory code of a eukaryotic genome. Nature 431(7004):99–104
Hartwell LH, Hopfield JJ et al (1999) From molecular to modular cell biology. Nature 402:C47–C52
Hasty J, McMillen D et al (2002) Engineered gene circuits. Nature 420:224–230
Ho Y, Gruhler A et al (2002) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415(6868):180–3
Holme P, Huss M et al (2003) Subnetwork hierarchies of biochemical pathways. Bioinformatics19:532–538
HuhWK, Falvo JV et al (2003) Global analysis of proteinlocalization in budding yeast. Nature 425(6959):686–91
Ihmels J (2002) Revealing modular organization in the yeast transcriptional network. Nat Genet31:370–377
ItoT (2001) A comprehensive two‐hybrid analysis to explore theyeast protein interactome. Proc Natl Acad Sci USA 98:4569–4574
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
Jacob F, Monod J (1961) Genetic regulatory mechanisms in the synthesis of proteins. J Mol Biol3:318–56
Jansen R (2003) A Bayesian networks approach for predicting protein‐protein interactions from genomic data. Science 302:449–453
JansenR, Greenbaum D et al (2002) Relating whole‐genomeexpression data with protein‐protein interactions. Genome Res 12(1):37–46
Jansen R, Lan N et al (2002) Integration of genomic datasets to predict protein complexes in yeast. J Struct Funct Genomics 2:71–81
Jansen R, Yu H et al (2003) A Bayesian networks approach for predicting protein‐protein interactions from genomic data. Science 302(5644):449–53
Jeong H, Mason SP et al (2001) Lethality and centrality in protein networks. Nature 411(6833):41–2
Jeong H, Tombor B et al (2000) The large-scale organization of metabolic networks. Nature407:651–654
JuvanP, Demsar J et al (2005) GenePath: from mutations togenetic networks and back. Nucleic Acids Res 33(Web Server issue):W749–52
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
Kitano H (2002) Computational systems biology. Nature 420:206–210
Koonin EV, Wolf YI et al (2002) The structure of the protein universe and genome evolution. Nature 420:218–223
Krogan NJ, Cagney G et al (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature440(7084):637–43
KumarA, Agarwal S et al (2002) Subcellular localization of theyeast proteome. Genes Dev 16(6):707–19
Launer RL, Wilkinson GN (1979) Robustness in statistics. Academic Press, New York
Lee I, Date SV et al (2004) A probabilistic functional network of yeast genes. Science 306(5701):1555–8
Lee TI, Rinaldi NJ et al (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298(5594):799–804
Li S (2004) A map of the interactome network of the metazoan, C elegans. Science 303:590–593
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
Lockhart DJ, Dong H et al (1996) Expression monitoring by hybridization to high‐density oligonucleotide arrays. Nat Biotechnol 14(13):1675–80
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
MacIsaac KD, Wang T et al (2006) An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinformatics 7:113
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
Maslov S, Sneppen K (2002) Specificity and stability in topology of protein networks. Science296:910–913
Ma'ayanA, Jenkins SL et al (2005) Formation of regulatorypatterns during signal propagation in a Mammalian cellular network.Science 309(5737):1078–83
Milo R, Itzkovitz S et al (2004) Superfamilies of evolved and designed networks. Science 303(5663):1538–42
Milo R, S Shen-Orr et al (2002) Network motifs: simple building blocks of complex networks. Science298(5594):824–7
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
Monod J, Jacob F (1961) Teleonomic mechanisms in cellular metabolism, growth, and differentiation. Cold Spring Harb Symp Quant Biol 26:389–401
Nadvornik P, Drozen V (1964) Models of Neurons and Neuron Networks. Act Nerv Super (Praha) 6:293–302
Newman MEJ (2002) Assortative mixing in networks. Phys RevLett 89:208701–208701
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
Novick A, Weiner M (1957) Enzyme Induction as an All-or-None Phenomenon. Proc Natl Acad Sci USA 43(7):553–66
Oltvai ZN, Barabasi AL (2002) Life's complexity pyramid. Science 298:763–764
Pastor‐SatorrasR, Vazquez A et al (2001) Dynamical andcorrelation properties of theInternet.Phys. Rev. Lett. 87:258701–258701
PtacekJ, Devgan G et al (2005) Global analysis of proteinphosphorylation in yeast. Nature 438(7068):679–84
Qi Y, Klein‐Seetharaman J et al (2005) Random forest similarity for protein‐protein interaction prediction from multiple sources. Pac Symp Biocomput:531–42
Rajewsky N (2006) microRNA target predictions in animals. NatGenet 38 Suppl:S8–13
Ravasz E, Barabasi AL (2003) Hierarchical organization in complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 67:026112–026112
Ravasz E, Somera AL et al (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551–5
Rives AW, Galitski T (2003) Modular organization of cellular networks. Proc Natl Acad Sci USA 100(3):1128–33
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
Rual JF, Venkatesan K et al (2005) Towards a proteome‐scale map of the human protein‐protein interaction network. Nature 437(7062):1173–8
Schena M, Shalon D et al (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235):467–470
Schleif R (2000) Regulation of the L‐arabinose operon of Escherichia coli. Trends Genet 16(12):559–65
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
Shen-Orr SS, Milo R et al (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31(1):64–8
Simon HA (1955) On a class of skew distribution functions. Biometrika 42:425–440
Simonis N, Gonze D et al (2006) Modularity of the transcriptional response of protein complexes in yeast. J Mol Biol363(2):589–610
SimonisN, van Helden J et al (2004) Transcriptional regulationof protein complexes in yeast. Genome Biol 5(5):R33
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
Smith LM, Sanders JZ et al (1986) Fluorescence detection in automated DNA sequence analysis. Nature 321(6071):674–9
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
Sole RV, R Pastor‐Satorras et al (2002) A Model of Large-Scale Proteome Evolution. Adv Complex Syst5:43–54
SoodP, Krek A et al (2006) Cell‐type‐specific signatures ofmicroRNAs on target mRNA expression. Proc Natl Acad Sci USA 103(8):2746–51
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
Spirin V, Mirny LA (2003) Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci USA 100(21):12123–8
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
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
StelzlU, Worm U et al (2005) A human protein‐proteininteraction network: a resource for annotating the proteome. Cell122(6):957–68
Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268–76
Stuart JM, Segal E et al (2003) A gene‐coexpression network for global discovery of conserved genetic modules. Science 302:249–255
Tanaka R (2005) Scale-rich metabolic networks. Phys Rev Lett 94(16):168101
Taylor RJ, Siegel AF et al (2007) Network motif analysis of a multi-mode genetic‐interaction network. Genome Biol 8(8):R160
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
Tong AH, Lesage G et al (2004) Global mapping of the yeast genetic interaction network. Science 303(5659):808–13
Tornow S, Mewes HW (2003) Functional modules by relating protein interaction networks and gene expression. Nucleic Acids Res31:6283–6289
TsangJ, Zhu J et al (2007) MicroRNA‐mediated feedback andfeedforward loops are recurrent network motifs in mammals. Mol Cell26(5):753–67
Uetz P, Giot L et al (2000) A comprehensive analysis of protein‐protein interactions in Saccharomyces cerevisiae. Nature 403(6770):623–7
Wagner A (2001) The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes. Mol Biol Evol 18(7):1283–92
Wagner A, Fell DA (2001) The small world inside large metabolic networks. Proc Biol Sci 268(1478):1803–10
Wall ME, Hlavacek WS et al (2004) Design of gene circuits: lessons from bacteria. Nature Rev Genet 5:34–42
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small‐world’ networks. Nature 393(6684):440–2
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
Winzeler EA (1999) Functional characterization of the S cerevisiae genome by gene deletion and parallel analysis. Science 285:901–906
Wong SL, Zhang LV et al (2004) Combining biological networks to predict genetic interactions. Proc Natl Acad Sci USA 101(44):15682–7
Wunderlich Z, Mirny LA (2006) Using the Topology of Metabolic Networks to Predict Viability of Mutant Strains. Biophys J 91(6):2304–2311
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
Yook SH, Oltvai ZN et al (2004) Functional and topological characterization of protein interaction networks. Proteomics 4(4):928–42
Yu H, Gerstein M (2006) Genomic analysis of the hierarchical structure of regulatory networks. Proc Natl Acad Sci USA 103(40):14724–31
Yu H, Luscombe NM et al (2003) Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet 19(8):422–7
Zhang L, King O et al (2005) Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network. J Biol 4(2):6
Zhang LV, Wong SL et al (2004) Predicting co‐complexed protein pairs using genomic and proteomic data integration. BMC Bioinformatics 5(1):38
Books and Reviews
Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet 5(2):101–13
Diestel R (2005) Graph Theory, 3rd edn. Springer, Heidelberg
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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
DOI: https://doi.org/10.1007/978-0-387-30440-3_38
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-75888-6
Online ISBN: 978-0-387-30440-3
eBook Packages: Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics