Biological networks

Current Opinion in Structural Biology

Volumn 13, Issue 2, 2003, Pages 193-202

  Alm, Eric ^a   Arkin, Adam P ^a  

^a LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MITOGEN ACTIVATED PROTEIN KINASE;

BIOCHEMISTRY; BIOLOGY; CHEMICAL MODEL; CHEMICAL REACTION; DATA ANALYSIS; DNA BINDING; ENGINEERING; GENETICS; GENOTYPE; GLYCOLYSIS; INFORMATION; MATHEMATICAL ANALYSIS; MICROORGANISM; MOLECULAR BIOLOGY; PHENOTYPE; POLYACRYLAMIDE GEL ELECTROPHORESIS; PRIORITY JOURNAL; PROTEIN MOTIF; PROTEIN STRUCTURE; QUALITATIVE ANALYSIS; REVIEW; SIGNAL TRANSDUCTION;

EID: 0037398136     PISSN: 0959440X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0959-440X(03)00031-9     Document Type: Review

References (67)

1. von Mering C., Krause R., Snel B., Cornell M., Oliver S., Fields S., Bork P. Comparative assessment of large-scale data sets of protein-protein interactions. Nature. 417:2002;399-403 The authors describe in detail the advantages and disadvantages of several high-throughput methods for detecting protein-protein interactions. They also compare predictions from each method with a high confidence reference set to estimate accuracy and coverage, and investigate possible sources of bias.
2. Uetz P., Giot L., Cagney G., Mansfield T., Judson R., Knight J., Lockshon D., Narayan V., Srinivasan M., Pochart P.et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature. 403:2000;623-627.
3. Ito T., Chiba T., Ozawa R., Yoshida M., Hattori M., Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA. 98:2001;4569-4574.
4. •].
5. •] describe two similar MS-based approaches to identifying components of multiprotein complexes. These approaches offer advantages over yeast two-hybrid screening because cooperative binding between three or more proteins is allowed and interactions need not occur in the nucleus. On the other hand, transient interactions may be difficult to detect using these methods.
6. Dandekar T., Snel B., Huynen M., Bork P. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem. Sci. 23:1998;324-328.
7. Overbeek R., Fonstein M., D'Souza M., Pusch G., Maltsev N. The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. USA. 96:1999;2896-2901.
8. Pellegrini M., Marcotte E., Thompson M., Eisenberg D., Yeates T. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl. Acad. Sci. USA. 96:1999;4285-4288.
9. Enright A., Iliopoulos I., Kyrpides N., Ouzounis C. Protein interaction maps for complete genomes based on gene fusion events. Nature. 402:1999;86-90.
10. Marcotte E., Pellegrini M., Thompson M., Yeates T., Eisenberg D. A combined algorithm for genome-wide prediction of protein function. Nature. 402:1999;83-86.
11. Tong A., Evangelista M., Parsons A., Xu H., Bader G., Pag N., Robinson M., Raghibizadeh S., Hogue C., Bussey H.et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science. 294:2001;2364-2368.
12. Mewes H., Frishman D., Guldener A., Mannhaupt G., Mayer K., Mokrejs M., Morgenstern B., Munsterkotter M., Rudd S., Weil B. MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 30:2002;31-34.
13. Csank C., Costanzo M., Hirschman J., Hodges P., Kranz J., Mangan M., O'Neill K., Robertson L., Skrzypek M., Brooks J., Garrels J. Three yeast proteome databases: YPD, PombePD, and CalPD (MycoPathPD). Methods Enzymol. 350:2002;347-373.
14. Bader G., Hogue C. BIND-a data specification for storing and describing biomolecular interactions, molecular complexes and pathways. Bioinformatics. 16:2000;465-477.
15. Xenarios I., Salwinski L., Duan X., Higney P., Kim S., Eisenberg D. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 30:2002;303-305.
16. Zhu H., Bilgin M., Bangham R., Hall D., Casamayor A., Bertone P., Lan N., Jansen R., Bidlingmaier S., Houfek T.et al. Global analysis of protein activities using proteome chips. Science. 293:2001;2101-2105.
17. Lee T., Rinaldi N., Robert F., Odom D., Bar-Joseph Z., Gerber G., Hannett N., Harbison C., Thompson C., Simon I.et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science. 298:2002;799-804 A map of protein-DNA interactions in yeast was built using high-throughput ChIP arrays. These interactions provide an overall picture of the transcriptional regulatory network in yeast and should prove an invaluable resource for future theoretical studies.
18. Barabasi A., Albert R. Emergence of scaling in random networks. Science. 286:1999;509-512.
19. Albert R., Jeong H., Barabasi A. Error and attack tolerance of complex networks. Nature. 406:2000;378-382.
20. Ravasz E., Somera A., Mongru D., Oltvai Z., Barabasi A. Hierarchical organization of modularity in metabolic networks. Science. 297:2002;1551-1555.
21. Luscombe N, Qian J, Zhang Z, Johnson T, Gerstein M: The dominance of the population by a selected few: power-law behaviour applies to a wide variety of genomic properties. Genome Biol 2002, 3:RESEARCH0040.
22. Rzhetsky A., Gomez S. Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome. Bioinformatics. 17:2001;988-996.
23. Hartwell L., Hopfield J., Leibler S., Murray A. From molecular to modular cell biology. Nature. 402:1999;C47-C52.
24. Watts D., Strogatz S. Collective dynamics of 'small-world' networks. Nature. 393:1998;440-442.
25. Fraser H., Hirsh A., Steinmetz L., Scharfe C., Feldman M. Evolutionary rate in the protein interaction network. Science. 296:2002;750-752.
26. Park J., Lappe M., Teichmann S. Mapping protein family interactions: intramolecular and intermolecular protein family interaction repertoires in the PDB and yeast. J. Mol. Biol. 307:2001;929-938.
27. Edwards A., Kus B., Jansen R., Greenbaum D., Greenblatt J., Gerstein M. Bridging structural biology and genomics: assessing protein interaction data with known complexes. Trends Genet. 18:2002;529-536.
28. Teichmann S., Rison S., Thornton J., Riley M., Gough J., Chothia C. The evolution and structural anatomy of the small molecule metabolic pathways in Escherichia coli. J. Mol. Biol. 311:2001;693-708.
29. Saqi M., Sternberg M. A structural census of metabolic networks for E. coli. J. Mol. Biol. 313:2001;1195-1206.
30. Rison S., Teichmann S., Thornton J. Homology, pathway distance and chromosomal localization of the small molecule metabolism enzymes in Escherichia coli. J. Mol. Biol. 318:2002;911-932.
31. Alves R., Chaleil R., Sternberg M. Evolution of enzymes in metabolism: a network perspective. J. Mol. Biol. 320:2002;751-770.
32. Ihmels J., Friedlander G., Bergmann S., Sarig O., Ziv Y., Barkai N. Revealing modular organization in the yeast transcriptional network. Nat. Genet. 31:2002;370-377 A new method for analyzing genome-wide expression data is applied to a large yeast data collection. A two-step approach is described that can overcome some problems associated with combinatorial regulation of yeast gene expression. In addition, the approach allows individual genes to be included in more than one functional module. Applying their algorithm to a comprehensive set of putative gene clusters, the authors are able to assign about 2200 yeast genes to 86 functional modules.
33. Pilpel Y., Sudarsanam P., Church G. Identifying regulatory networks by combinatorial analysis of promoter elements. Nat. Genet. 29:2001;153-159.
34. Hughes J., Estep P., Tavazoie S., Church G. Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J. Mol. Biol. 296:2000;1205-1214.
35. Snel B., Bork P., Huynen M. The identification of functional modules from the genomic association of genes. Proc. Natl. Acad. Sci. USA. 99:2002;5890-5895 The authors analyzed protein interaction networks derived from the co-occurrence of genes in operons by partitioning them into functional modules. In doing so, they identify 'linker' proteins, which tend to connect functionally distinct clusters into one large component, and remove connections from these proteins to preserve the functional homogeneity within modules.
36. Moreno-Hagelsieb G., Collado-Vides J. A powerful non-homology method for the prediction of operons in prokaryotes. Bioinformatics. 18(suppl 1):2002;S329-S336.
37. Zheng Y., Szustakowski J., Fortnow L., Roberts R., Kasif S. Computational identification of operons in microbial genomes. Genome Res. 12:2002;1221-1230.
38. Sabatti C., Rohlin L., Oh M., Liao J. Co-expression pattern from DNA microarray experiments as a tool for operon prediction. Nucleic Acids Res. 30:2002;2886-2893.
39. Ermolaeva M., White O., Salzberg S. Prediction of operons in microbial genomes. Nucleic Acids Res. 29:2001;1216-1221.
40. Craven M., Page D., Shavlik J., Bockhorst J., Glasner J. A probabilistic learning approach to whole-genome operon prediction. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8:2000;116-127.
41. Rajewsky N., Socci N., Zapotocky M., Siggia E. The evolution of DNA regulatory regions for proteo-gamma bacteria by interspecies comparisons. Genome Res. 12:2002;298-308.
42. McCue L., Thompson W., Carmack C., Ryan M., Liu J., Derbyshire V., Lawrence C. Phylogenetic footprinting of transcription factor binding sites in proteobacterial genomes. Nucleic Acids Res. 29:2001;774-782.
43. McGuire A., Hughes J., Church G. Conservation of DNA regulatory motifs and discovery of new motifs in microbial genomes. Genome Res. 10:2000;744-757.
44. Gelfand M., Koonin E., Mironov A. Prediction of transcription regulatory sites in archaea by a comparative genomic approach. Nucleic Acids Res. 28:2000;695-705.
45. Milo R., Shen-Orr S., Itzkovitz S., Kashtan N., Chklovskii D., Alon U. Network motifs: simple building blocks of complex networks. Science. 298:2002;824-827.
46. Shen-Orr S., Milo R., Mangan S., Alon U. Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31:2002;64-68 The authors extend the notion of motifs to the genetic regulatory network of E. coli and find that much of the protein-DNA interaction network in this organism is built from recurring patterns of connections. In particular, they describe and analyze three such patterns that occur much more often than would be expected in a random network.
47. Salgado H., Santos-Zavaleta A., Gama-Castro S., Millan-Zarate D., Diaz-Peredo E., Sanchez-Solano F., Perez-Rueda E., Bonavides-Martinez C., Collado-Vides J. RegulonDB (version 3.2): transcriptional regulation and operon organization in Escherichia coli K-12. Nucleic Acids Res. 29:2001;72-74.
48. Hawkins J: Gene Structure and Expression. Cambridge, UK: Cambridge University Press; 1996.
49. Schleif R. Regulation of the L-arabinose operon of Escherichia coli. Trends Genet. 16:2000;559-565.
50. Ge H., Liu Z., Church G., Vidal M. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat. Genet. 29:2001;482-486.
51. Ideker T., Ozier O., Schwikowski B., Siegel A. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics. 18(suppl 1):2002;S233-S240.
52. Ideker T., Thorsson V., Ranish J., Christmas R., Buhler J., Eng J., Bumgarner R., Goodlett D., Aebersold R., Hood L. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science. 292:2001;929-934.
53. Grigoriev A. A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae. Nucleic Acids Res. 29:2001;3513-3519.
54. Steffen M., Petti A., Aach J., D'haeseleer P., Church G. Automated modelling of signal transduction networks. BMC Bioinformatics. 3:2002;34.
55. Elowitz M., Leibler S. A synthetic oscillatory network of transcriptional regulators. Nature. 403:2000;335-338.
56. Gardner T., Cantor C., Collins J. Construction of a genetic toggle switch in Escherichia coli. Nature. 403:2000;339-342.
57. Becskei A., Serrano L. Engineering stability in gene networks by autoregulation. Nature. 405:2000;590-593.
58. Becskei A., Seraphin B., Serrano L. Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO J. 20:2001;2528-2535.
59. Elowitz M., Levine A., Siggia E., Swain P. Stochastic gene expression in a single cell. Science. 297:2002;1183-1186.
60. Thattai M., van Oudenaarden A. Attenuation of noise in ultrasensitive signaling cascades. Biophys J. 82:2002;2943-2950.
61. Ozbudak E., Thattai M., Kurtser I., Grossman A., van Oudenaarden A. Regulation of noise in the expression of a single gene. Nat. Genet. 31:2002;69-73.
62. Arkin A., Ross J., McAdams H. Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics. 149:1998;1633-1648.
63. Guet C., Elowitz M., Hsing W., Leibler S. Combinatorial synthesis of genetic networks. Science. 296:2002;1466-1470 The authors surveyed the phenotypic behaviors available to a three-node genetic regulatory network by constructing a combinatorial library of promoters and transcriptional regulators.
64. von Dassow G., Meir E., Munro E., Odell G. The segment polarity network is a robust developmental module. Nature. 406:2000;188-192.
65. Brown P., Botstein D. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21:1999;33-37.
66. Goodman L. Hypothesis-limited research. Genome Res. 9:1999;673-674.
67. Allen J. Bioinformatics and discovery: induction beckons again. Bioessays. 23:2001;104-107.