Biological network analysis: Insights into structure and functions

Briefings in Functional Genomics

Volumn 11, Issue 6, 2012, Pages 434-442

  Ma, Xiaoke ^a   Gao, Lin ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Biological functions; Biological networks; Structure analysis

Indexed keywords

ANALYTIC METHOD; ARTICLE; BIOLOGICAL FUNCTIONS; BIOLOGICAL NETWORK ANALYSIS; BIOLOGY; DYNAMICS; GENE INTERACTION; STRUCTURE ANALYSIS;

COMPUTATIONAL BIOLOGY; GENE REGULATORY NETWORKS; HUMANS;

EID: 84870374943     PISSN: 20412649     EISSN: 20412657     Source Type: Journal    
DOI: 10.1093/bfgp/els045     Document Type: Article

References (116)

1. Barabási AL, Culbahce N, Loscaizo J. Network medicine: a network-based approach to human disease. Nat Genet 2012; 12:56-68.
2. Bonetta L. Getting up close and personal with your genome. Cell 2008;133:753-6.
3. Wang X, Wei X, Thijssen B, et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat Biotechnol 2012;30:159-64.
4. Gavin AC, Bosche M, Krause R, et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002;415:141-7.
5. Ho Y, Gruhler A, Heibut A, et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002;415:180-3.
6. Iyer VR, Horak CE, Scafe CS, et al. Genomic binding sites of the yeast cell-cycle transcription factors sbf and mbf. Nature 2001;409:533-8.
7. Rual JF, Venkatesan K, Hao T, et al. Towards a proteomescale map of the human protein-protein interaction network. Nature 2005;437:1173-8.
8. Stelzl U, Worm U, Lalowski M, et al. A human proteinprotein interaction network: a resource for annotating the proteome. Cell 2005;122:957-68.
9. Tong AHY, Evangelista M, Parsons AB, et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 2001;294:2364-8.
10. Nikitin A, Egorov S, Daraselia N, et al. Pathway studio-the analysis and navigation of molecular networks. Bioinformatics 2003;19:2155-7.
11. Sharan R, Ideker T. Modeling cellular machinery through biological network comparison. Nat Comput Biol 2006;24: 427-33.
12. von Mering C, Krause R, Snel B, et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 2002;417:399-403.
13. Schadt EE, Friend SH, Shaywitz DA. A network view of disease and compound screening. Nat Rev Drug Discovery 2009;8:286-95.
14. Carninci P, Kasukawa T, Katayama S, et al. The transcriptional landscape of the mammalian genome. Science 2005; 309:1559-63.
15. Duarte NC, Becker SA, Jamshidi N, et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA 2007;104: 1777-82.
16. Jeong H, Tombor B, Albert R, et al. The large-scale organization of metabolic networks. Nature 2000;407:651-4.
17. Yu H, Kim M, Sprecher E, et al. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLos Comput Biol 2007;3:e59.
18. Watts DJ, Strogztz SH. Collective dynamics of 'smallworld' networks. Nature 1998;393:440-2.
19. Barabási AL, Albert R. Emergence of scaling random networks. Science 1999;286:509-12.
20. Milo R, Shen-Orr S, Itzkovitz S, et al. Network motifs: simple building blocks of complex networks. Science 2002; 298:824-7.
21. Girvan M, Newman MEJ. Community structure in social and biological networks. Proc Natl Acad Sci USA 2002;99: 7821-6.
22. Milo R, Itzkovitz S, Kashtan N, et al. Superfamilies of evolved and designed networks. Science 2004;303: 1538-42.
23. Shen-Orr SS, Milo R, Mangan S, et al. Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 2002;31:64-8.
24. Wuchty S, Oltvai ZN, Barabási AL, et al. Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat Genet 2003;35:176-9.
25. Yeger-Lotem E, Sattatch S, Kashtan N, et al. Network motifs in integrated cellular networks of transcriptionregulation and protein-protein interaction. Proc Natl Acad SciUSA 2004;101:5934-9.
26. Berg J, Lässig M. Local graph alignment and motif search in biological networks. Proc Natl Acad Sci USA 2004; 101(41):14689-94.
27. Jiang R, Tu Z, Chen T, et al. Network motif identification in stochastic networks. Proc Natl Acad Sci USA 2006; 103(25):9404-9.
28. Ciriello G, Guerra C. A review on models and algorithms for motif discovery in protein-protein interaction networks. Brief Funct Genomics Proteomics 2008;7(2):147-56.
29. Wong E, Baur B, Quader S, et al. Biological network motif detection: principles and practice. Brief Bioinformatics 2011; 13(2):202-15.
30. Shoval O, Alon U. Snapshot: network motifs. Cell 2010; 143:326-326, e1.
31. Alon U. Network motifs: theory and experimental approaches. Nat RevGenet 2007;8:450-61.
32. Wernicke S. Efficient detection of network motifs. IEEE/ ACMTrans Comput Biol Bioinformatics 2006;3(4):347-59.
33. Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet 2004;5: 101-13.
34. Hartwell LH, Hopfield JJ, Murray AW. From molecular to modular cell biology. Nature 1999;402:C47-C52.
35. Ravasz E, Somera AL, Mongru DA, et al. Hierarchical organization of modularity in metabolic networks. Science 2002;297:1551-5.
36. River AW, Galitski T. Modular organization of cellular networks. Proc Natl Acad Sci USA 2003;100:1128-33.
37. Bu D, Yi Z, Lun C, et al. Topological structure analysis of the protein-protein interaction network in budding yeast. Nucleic Acids Res. 2003;31:2443-50.
38. Spirin V, Mirny LA. Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci USA 2003;100:12123-8.
39. Guimera R, Amaral LAN. Functional cartography of complex metabolic networks. Nature 2005;433(7028):895-900.
40. Yamada T, Kanehisa M, Goto S. Extraction of phylogentic network modules from the metabolic network. BMC Bioinformatics 2006;7:130.
41. Arnau V, Mars S, Martin I. Iterative cluster analysis of protein interaction data. Bioinformatics 2005;31:364-78.
42. Asur S, Ucar D, Parthasarathy S. An ensemble framework for clustering protein-protein interaction networks. Bioinformatics 2007;23(13):29-40.
43. Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 2003;4(1):2.
44. Brohée S, van Helden J. Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 2006;7:1-19.
45. Jeongah Y, Anselm B, Kyongbum L. An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality. Bioinformatics 2006;22(24):3106-8.
46. King AD, Przulj N, Jurisica I. Protein complex prediction via cost-based clustering. Bioinformatics 2004; 20(17):3013-20.
47. Li W, Liu Y, Huang HC, et al. Dynamical systems for discovering protein complexes and functional modules from biological networks. IEEE/ACM Trans Comput Biol Bioinformatics 2007;4:233-50.
48. Ma X, Gao L. Discovering protein complexes in protein interaction networks via exploring the weak ties. BMC Systems Biol 2012;6(s1):s6.
49. Ma X, Gao L. Predicting protein complexes in protein interaction networks using acore-attachment algorithm based on graph communicability. Information Sci 2012;189: 233-54.
50. Pereira-Leal JB, Enright AJ, Ouzounis CA. Detection of functional modules from protein interaction networks. Proteins 2004;54(1):49-57.
51. Qi Y, Balem F, Faloutsos C, et al. Protein complex identification by supervised graph local clustering. Bioinformatics 2008;24:250-8.
52. Yu L, Gao L, Kong C. Identification of core-attachment complexes based on mining maximal frequent patterns in protein-protein interaction networks. Proteomics 2011; 11(19):3826-34.
53. Yu L, Gao L, Peng S. Hybrid clustering algorithm for identifying modularity in protein-protein interaction networks. Int J Data Mining Bioinformatics 2010; 5(4):600-15.
54. Kelley BP, Sharan R, Karp RM, et al. Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc Natl Acad Sci USA 2003; 100(20):11394-9.
55. Sharan R, Suthram S, Kelley RM, et al. Conserved patterns of protein interaction in multiple species. Proc Natl Acad Sci USA 2005;102(6):1974-9.
56. Singh R, Xu JB, Berger B. Pairwise global alignment of protein interaction networks by matching neighborhood topology. RECOMBLNBI 2007;4453:16-31.
57. Flannick J, Novak A, Do CB, et al. Automatic parameter learning for multiple network alignment. RECOMB LNBI 2008;4955:221-31.
58. Liao CS, Kanghao L, Michael B, et al. Isorankn: spectral methods for global alignment of multiple protein networks. Bioinformatics 2009;25(12):i253-i258.
59. Kuchaiev O, Milenkoviál V, Memisevićl V, et al. Topological network alignment uncovers biological function and phylogeny. J Royal Society Interface 2010;7: 1341-54.
60. Kuchaiev O, Przulj N. Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics 2011;27(10):1390-6.
61. Memisevic V, Przulj N. C-GRAAL: common-neighborsbased global graph alignment of biological networks. Integr Biol 2012;4:734-43.
62. Zaslavskiy M, Bach F, Vert JP. Global alignment of protein-protein interaction networks by graph matching methods. Bioinformatics 2009;25(12):1259-67.
63. Flannick J, Novak A, Srinivasan BS, et al. Graemlin: general and robust alignment of multiple large interaction networks. Genome Res 2006;16:1169-81.
64. Wang B, Gao L. Global network alignment based on multiple hub seeds. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Atlanta, Georgia, USA, 2011:234-7.
65. Kelley BP, Yuan B, Lewitter F, et al. Pathblast: a tool for alignment of protein interaction networks. Nucleic Acids Res 2004;32:w83-w88.
66. Liang Z, Xu M, Teng MK. Comparison of protein interaction networks reveals species conservation and divergence. BMC Bioinformatics 2006;7:457.
67. Koyutürk M, Kim Y, Topkara U, et al. Pairwise alignment of protein interaction networks. J Comput Biol 2006; 13(2):1631-9.
68. Li ZP, Zhang SH, Wang Y, et al. Alignment of molecular networks by integer quadratic programming. Bioinformatics 2007;23(13):1631-9.
69. Goh KI, Cusick ME, Valle D, et al. The human disease network. Proc Natl Acad Sci USA 2007;104:8685-90.
70. Oti M, Brunner HG. The modular nature of genetic disease. Clin Genet 2007;71:1-11.
71. van Driel MA, Bruggeman J, Vriend G, et al. A text-mining analysis of the human phenome. Eur J Human Genet 2006;14: 535-42.
72. Oti M, Snel B, Huynen MA, et al. Predicting disease genes using protein-protein interactions. J Med Genet 2006;43: 691-8.
73. Lage K, Karlberg EO, Storling ZM, et al. A human phenome-interactome network of protein complexes implicated in genetic disorder. Nat Biotechnol 2007; 25(3):309-16.
74. Sun P, Gao L, Han S. Prediction of human disease-related gene clusters by clustering analysis. Int J Biol Sci 2011; 7(1):61-73.
75. Wu X, Jiang R, Zhang M, et al. Network-based global inference of human disease genes. Mol Syst Biol 2008;4:189.
76. Erten S, Bebek G, Koyuturk M.. Disease gene prioritization based on topological similarity in protein-protein interaction networks. In: Proceedings of the 15th Annual International Conference on Research in Computational Molecular Biology. Berlin, Germany, 2011:54-68.
77. Guo X, Gao L, Yang X, et al. A computational method based on the integration of heterogeneous networks for predicting disease-gene associations. PLoSOne 2011;6(9):e34171.
78. Kohler S, Bauer S, Horn D, et al. Walking the interactome for prioritization of candidate disease genes. Am J Human Genet 2008;82:949-58.
79. Vanuna O, Magger O, Ruppin E, et al. Associating genes and protein complex with disease via network propagation. PLos Comput Biol 2010;6(1):8685-90.
80. Adie EA, Adams RR, Evans KL, et al. Speeding disease gene discovery by sequence-based candidate prioritization. BMC Bioinformatics 2005;6:55.
81. Gill R, Datta S. A statistical framework for differential network analysis from microarray data. BMC Bioinformatics 2010;11:95.
82. Xu JZ, Li YJ. Discovering disease-genes by topological features in human proteincprotein interaction network. Bioinformatics 2006;22:2800-5.
83. Wu XB, Liu QF, Jiang R. Align human interactome with phenome to identify causative genes and networks underlying disease families. Bioinformatics 2009;25:98-104.
84. Chuang HY, Lee E, Liu YT, et al. Network-based classification of breast cancer metastasis. Mol Syst Biol 2007;3:140.
85. Edelman EJ, Guinney J, Chi JT, et al. Modeling cancer progression via pathway dependencies. PLos Comput Biol 2008;4:e28.
86. Lin CC. Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy. BMC Syst Biol 2010;4:138.
87. Nibble RK, Koyutürk M, Chance MR. An integrativeomics approach to identify functional sub-networks in human colorectal cancer. Plos Comput Biol 2010; 6(1):e1000639.
88. Taylor IW, Linding R, Warde-Farley D, et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 2009;27(2):199-204.
89. Alexander RP, Kim PM, Emonet T, et al. Understanding modularity in molecular networks requires dynamics. Sci Signal 2009;2(81):pe44.
90. Vespignani A. Evolution thinks modular. Nat Genet 2003; 35(2):118-9.
91. Komurov K, White M. Revealing static and dynamic modular architecture of the eukaryotic protein interaction network. Mol Syst Biol 2007;3:110.
92. Wang X, Dalkic E, Wu M. Gene-module level analysis: identification to networks and dynamics. Curr Opin Biotechnol 2008;19(5):482-91.
93. Kovács IA, Palotai R, Szalay MS, et al. Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics. PLoS One 2010;5(9):e12528.
94. Park Y, Moore C, Bader JS. Dynamic networks from hierarchical Bayesian graph clustering. Plos One 2010;5: e8118.
95. Ay F, Dinh TN, Thai MT, et al. Finding dynamic modules of biological regulatory networks. In: Proceedings of the 2010 IEEE International Conference on Bioinformatics and Bioengineering. Philadelphia, PA, USA, 2010:136-43.
96. Xiong H, Choe Y. Dynamical pathway analysis. BMCSyst Biol 2008;2:9.
97. Sol A, Balling R, Hood L, et al. Disease as network perturbations. Curr Opin Biotechnol 2010;21:566-71.
98. Erler JT, Linding R. Network-based drugs and biomarkers. J Pathol 2010;220:290-6.
99. Tomlins SA, Mehra R, Rhodes DR, et al. Integrative molecular concept modeling of prostate cancer progression. Nat Genet 2006;39:41-51.
100. Tyson JJ, Baumann WT, Chen C, et al. Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells. Nat Rev Cancer 2011;11:523-32.
101. Chen LL, Blumm N, Christakis NA, et al. Cancer metastasis networks and the prediction of progression patterns. Br J Cancer 2009;101:749-58.
102. Hidalgo CA, Blumm N, Barabási AL, et al. A dynamic network approach for the study of human phenotypes. Plos Comput Biol 2009;5(4):e1000353.
103. Echadt EE, Linderman MD, Sorenson J, et al. Computational solutions to large-scale data management and analysis. Nat RevGenet 2010;11:647-57.
104. Eisen MB, Spellman PT, Brown PO, et al. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad SciUSA 1998;95(25):14863-8.
105. Maathuis MH, Colombo D, Kalisch M, et al. Predicting causal effects in large-scale systems from observational data. NatMethods 2010;7:247-8.
106. Mahoney M, Lim LH, Carlsson GE. Algorithmic and statistical challenges in modern large-scale data analysis are the focus of MMDS 2008. KDDExplorations 2008;10:57-60.
107. Tanay A, Steinfeld I, Kupiec M, et al. Integrative analysis of genome-wide experiments in the context of a large high-throughput data compendium. Mol Syst Biol 2005 doi:10.1038/msb4100005.
108. Yarkoni T, Poldrack RA, Nichols T, et al. Large-scale automated synthesis of human functional neuroimaging data. NatMethods 2011;8:665-70.
109. Hwang D, Rust AG, Ramsey S, et al. A data integration methodolgy for systems biology. Proc Natl Acad Sci USA 2005;102(48):17296-301.
110. Lee I, Blom UM, Wang PI, et al. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res 2011;21(7):1109-21.
111. Zhu X, Gerstein M, Snyder M. Getting connected: analysis and principles of biological networks. Genes Dev 2007;21: 1010-24.
112. Przytycka TM, Slonim MSDK. Toward the dynamic interactome: it's about time. Brief Bioinformatics 2010;11: 15-29.
113. Le'bre S. Inferring dynamic bayesian network with low order independencies. Statistical Appl Genet Mol Biol 2009; 8:9.
114. Yoshida RF, Imoto S, Higuch T. Estimating timedependent gene networks from time series microarray data by dynamic linear models with markov switching. In: Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference. Washington, DC, USA, 2005:289-98.
115. Le'bre S, Becq J, Devaux F, et al. Statistical inference of the time-varying structure of gene-regulation networks. BMC Syst Biol 2010;4:130.
116. Chechik G, Oh E, Rando O, et al. Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network. Nat Biotechnol 2008;26: 1251-59.