Tutorial on biological networks

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volumn 2, Issue 4, 2012, Pages 298-325

  Vital Lopez, Francisco G ^a   Memišević, Vesna ^a   Dutta, Bhaskar ^a  

^a HENRY M JACKSON FOUNDATION FOR THE ADVANCEMENT OF MILITARY MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA INTEGRATION; DATA VISUALIZATION; PROTEINS; SIGNALING; TOPOLOGY;

BIOLOGICAL NETWORKS; GENETIC INTERACTION; HIGH THROUGHPUT TECHNOLOGY; NETWORK CONSTRUCTION; PROTEIN-PROTEIN INTERACTIONS; TOPOLOGICAL ANALYSIS; TRANSLATIONAL RESEARCH; VISUALIZATION TOOLS;

MOLECULAR BIOLOGY;

EID: 84873156585     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.1061     Document Type: Review

References (248)

1. Barabasi AL. Scale-free networks: a decade and beyond. Science 2009, 325:412-413.
2. Butts CT. Revisiting the foundations of network analysis. Science 2009, 325:414-416.
3. Bascompte J. Disentangling the web of life. Science 2009, 325:416-419.
4. Ostrom E. A general framework for analyzing sustainability of social-ecological systems. Science 2009, 325:419-422.
5. Vespignani A. Predicting the behavior of technosocial systems. Science 2009, 325:425-428.
6. Sharan R, Ideker T. Modeling cellular machinery through biological network comparison. Nat Biotechnol 2006, 24:427-433.
7. Balazsi G, Barabasi AL, Oltvai ZN. Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli. Proc Natl Acad Sci USA 2005, 102:7841-7846.
8. Veiga DF, Dutta B, Balazsi G. Network inference and network response identification: moving genomescale data to the next level of biological discovery. Mol Biosyst 2010, 6:469-480.
9. Dutta B, et al. Time-series integrated "omic" analyses to elucidate short-term stress-induced responses in plant liquid cultures. Biotechnol Bioeng 2009, 102:264-279.
10. Hirai MY, et al. Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci USA 2004, 101:10205-10210.
11. Chuang HY, et al. Network-based classification of breast cancer metastasis. Mol Syst Biol 2007, 3:140.
12. Kanehisa M, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res 2008, 36(Database issue):D480-D484.
13. Caspi R, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 2010, 38(Database issue):D473-D479.
14. Wingender E. The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation. Brief Bioinform 2008, 9:326-332.
15. Griffith OL, et al. ORegAnno: an open-access community-driven resource for regulatory annotation. Nucleic Acids Res 2008, 36(Database issue):D107-D113.
16. Mewes HW, et al.MIPS: a database for genomes and protein sequences. Nucleic Acids Res 2002, 30:31-34.
17. Salwinski L, et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res 2004, 32(Database issue):D449-D451.
18. Zanzoni A, et al. MINT: a Molecular INTeraction database. FEBS Lett 2002, 513:135-140.
19. Kerrien S, et al. IntAct-open source resource for molecular interaction data. Nucleic Acids Res 2007, 35(Database issue):D561-D565.
20. Breitkreutz BJ, et al. The BioGRID Interaction Database: 2008 update. Nucleic Acids Res 2008, 36(Database issue):D637-D640.
21. Peri S, et al. Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res 2003, 13:2363-2371.
22. Yu J, et al. DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions. BMC Genomics 2008, 9:461.
23. Su C, et al. Bacteriome.org-an integrated protein interaction database for E. coli. Nucleic Acids Res 2008, 36(Database issue):D632-D636.
24. Brown KR, Jurisica I. Online predicted human interaction database. Bioinformatics 2005, 21:2076-2082.
25. Koh JL, et al. DRYGIN: a database of quantitative genetic interaction networks in yeast. Nucleic Acids Res 2010, 38(Database issue):D502-D507.
26. Berg JM, Tymoczko JL, Stryer L. Biochemistry. 6th ed. New York: W.H. Freeman; 2007.
27. Feist AM, et al. Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 2009, 7:129-143.
28. Ruppin E, et al. Metabolic reconstruction, constraintbased analysis and game theory to probe genomescale metabolic networks. Curr Opin Biotechnol 2010, 21:502-510.
29. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28:27-30.
30. Krieger CJ, et al.MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res 2004, 32(Database issue):D438-D442.
31. Maglott D, et al. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res 2007, 39(Database issue):D52-D57.
32. Stoesser G, et al. The EMBL Nucleotide Sequence Database. Nucleic Acids Res 1999, 27:18-24.
33. Schilling CH, et al. Genome-scale metabolic model of Helicobacter pylori 26695. J Bacteriol 2002, 184:4582-4593.
34. Ramakrishna R, et al. Flux-balance analysis of mitochondrial energy metabolism: consequences of systemic stoichiometric constraints. Am J Physiol Regul Integr Comp Physiol 2001, 280:R695-R704.
35. Varma A, Boesch BW, Palsson BO. Biochemical production capabilities of Escherichia coli. Biotechnol Bioeng 1993, 42:59-73.
36. Becker SA, et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2007, 2:727-738.
37. Gianchandani EP. Chavali AK, and Papin JA. The application of flux balance analysis in systems biology. Wiley Interdiscip Rev Syst Biol Med 2:372-382.
38. Arda HE, Walhout AJ. Gene-centered regulatory networks. Brief Funct Genomics 2010, 9:4-12.
39. Park PJ. ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 2009, 10:669-680.
40. Lee WP, Tzou WS. Computational methods for discovering gene networks from expression data. Brief Bioinform 2009, 10:408-423.
41. Chiang JH, Yu HC. MeKE: discovering the functions of gene products from biomedical literature via sentence alignment. Bioinformatics 2003, 19:1417-1422.
42. Laubenbacher R, Stigler B. A computational algebra approach to the reverse engineering of gene regulatory networks. J Theor Biol 2004, 229:523-537.
43. Mehra S, Hu WS, Karypis G. A Boolean algorithm for reconstructing the structure of regulatory networks. Metab Eng 2004, 6:326-339.
44. Li P, et al. Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks. BMC Bioinformatics 2007, 8(Suppl 7):S13.
45. Hartemink AJ, et al. Combining location and expression data for principled discovery of genetic regulatory network models. Pac Symp Biocomput 2002, 437-449.
46. Walhout AJ. What does biologically meaningful mean? A perspective on gene regulatory network validation. Genome Biol 2011, 12:109.
47. De Smet R, Marchal K. Advantages and limitations of current network inference methods. Nat Rev Microbiol 2010, 8:717-729.
48. MarbachD, et al. Revealing strengths andweaknesses of methods for gene network inference. Proc Natl Acad Sci USA 2010, 107:6286-6291.
49. Alm E, Arkin AP. Biological networks. Curr Opin Struct Biol 2003, 13:193-202.
50. Claverie JM. Gene number. What if there are only 30,000 human genes? Science 2001, 291:1255-1257.
51. Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet 2004, 5:101-113.
52. Ideker T, Sharan R. Protein networks in disease. Genome Res 2008, 18:644-652.
53. Schwartz AS, et al. Cost-effective strategies for completing the interactome. NatMethods 2009, 6:55-61.
54. vonMering C, et al. Comparative assessment of largescale data sets of protein-protein interactions. Nature 2002, 417:399-403.
55. Ito T, et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 2001, 98:4569-4574.
56. Ito T, et al. 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 2000, 97:1143-1147.
57. Uetz P, et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000, 403:623-627.
58. Li S, et al. A map of the interactome network of the metazoan C. elegans. Science 2004, 303:540-543.
59. Rain JC, et al. The protein-protein interaction map of Helicobacter pylori. Nature 2001, 409:211-215.
60. Stelzl U, et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell 2005, 122:957-968.
61. Gavin AC, et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002, 415:141-147.
62. Butland G, et al. Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature 2005, 433:531-537.
63. ZhuH, et al. Global analysis of protein activities using proteome chips. Science 2001, 293:2101-2105.
64. Barrios-Rodiles M, et al. High-throughput mapping of a dynamic signaling network in mammalian cells. Science 2005, 307:1621-1625.
65. Ho Y, et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002, 415:180-183.
66. Hughes TR, et al. Functional discovery via a compendium of expression profiles. Cell 2000, 102:109-126.
67. Rigaut G, et al. A generic protein purification method for protein complex characterization and proteome exploration. Nat Biotechnol 1999, 17:1030-1032.
68. Tarassov K, et al. An in vivo map of the yeast protein interactome. Science 2008, 320:1465-1470.
69. Ruffner H, Bauer A, Bouwmeester T. Human protein-protein interaction networks and the value for drug discovery. Drug Discov Today 2007, 12:709-716.
70. Giot L, et al. A protein interaction map of Drosophila melanogaster. Science 2003, 302:1727-1736.
71. Memisevic V, Milenkovic T, Przulj N. An integrative approach to modeling biological networks. J Integr Bioinform 2010, 7.
72. Ewing RM, et al. Large-scale mapping of human protein-protein interactions by mass spectrometry. Mol Syst Biol 2007, 3:89.
73. Dandekar T, et al. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem Sci 1998, 23:324-328.
74. Deng M, et al. Inferring domain-domain interactions from protein-protein interactions. Genome Res 2002, 12:1540-1548.
75. Enright AJ, et al. Protein interaction maps for complete genomes based on gene fusion events. Nature 1999, 402:86-90.
76. Gerstein M. Patterns of protein-fold usage in eight microbial genomes: a comprehensive structural census. Proteins 1998, 33:518-534.
77. Jansen R, et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 2003, 302:449-453.
78. Lee H, et al. An integrated approach to the prediction of domain-domain interactions. BMC Bioinformatics 2006, 7:269.
79. Marcotte EM, et al. Detecting protein function and protein-protein interactions from genome sequences. Science 1999, 285:751-753.
80. Pitre S, et al. Computational methods for predicting protein-protein interactions. Adv Biochem Eng Biotechnol 2008, 110:247-267.
81. Shen J, et al. Predicting protein-protein interactions based only on sequences information. Proc Natl Acad Sci USA 2007, 104:4337-4341.
82. Valencia A, Pazos F. Computational methods for the prediction of protein interactions. Curr Opin Struct Biol 2002, 12:368-373.
83. Wang H, et al. InSite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale. Genome Biol 2007, 8:R192.
84. Yu H, et al. High-quality binary protein interaction map of the yeast interactome network. Science 2008, 322:104-110.
85. Krogan NJ, et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 2006, 440:637-643.
86. Collins SR, et al. Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Mol Cell Proteomics 2007, 6:439-450.
87. Wang H, et al. A complex-based reconstruction of the Saccharomyces cerevisiae interactome. Mol Cell Proteomics 2009, 8:1361-1381.
88. Yu X, et al. A novel scoring approach for protein copurification data reveals high interaction specificity. PLoS Comput Biol 2009, 5:e1000515.
89. Schelhorn SE, et al. Inferring physical protein contacts from large-scale purification data of protein complexes. Mol Cell Proteomics 2011.
90. Cusick ME, et al. Literature-curated protein interaction datasets. Nat Methods 2009, 6:39-46.
91. Simonis N, et al. Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network. Nat Methods 2009, 6:47-54.
92. Venkatesan K, et al. An empirical framework for binary interactome mapping. NatMethods 2009, 6:83-90.
93. Futschik ME, Chaurasia G, Herzel H. Comparison of human protein-protein interaction maps. Bioinformatics 2007, 23:605-611.
94. Seringhaus MR, Cayting PD, Gerstein MB. Uncovering trends in gene naming. Genome Biol 2008, 9:401.
95. Chuang HY, Hofree M, Ideker T. A decade of systems biology. Annu Rev Cell Dev Biol 2010, 26:721-44.
96. Mani R, et al. Defining genetic interaction. Proc Natl Acad Sci USA 2008, 105:3461-3466.
97. Kelley R, Ideker T. Systematic interpretation of genetic interactions using protein networks. Nat Biotechnol 2005, 23:561-566.
98. Hartman JLT, Garvik B, Hartwell L. Principles for the buffering of genetic variation. Science 2001, 291:1001-1004.
99. Vidal M, Cusick ME, Barabasi AL. Interactome networks and human disease. Cell 2011, 144:986-998.
100. Tong AH, et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 2001, 294:2364-2368.
101. Tong AH, et al. Global mapping of the yeast genetic interaction network. Science 2004, 303:808-813.
102. Ooi SL, Shoemaker DD, Boeke JD, DNAhelicase gene interaction network defined using synthetic lethality analyzed by microarray. Nat Genet 2003, 35:277-286.
103. Schuldiner M, et al. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 2005, 123:507-519.
104. Bommi-Reddy A, et al. Kinase requirements in human cells. III. Altered kinase requirements in VHL-/-cancer cells detected in a pilot synthetic lethal screen. Proc Natl Acad Sci USA 2008, 105:16484-16489.
105. Lehner B, et al. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nat Genet 2006, 38:896-903.
106. Yang WS, Stockwell BR. Inhibition of casein kinase 1-epsilon induces cancer-cell-selective, PERIOD2-dependent growth arrest. Genome Biol 2008, 9:R92.
107. Zender L, et al. An oncogenomics-based in vivo RNAi screen identifies tumor suppressors in liver cancer. Cell 2008, 135:852-864.
108. Hyduke DR, Palsson BO. Towards genome-scale signalling network reconstructions. Nat Rev Genet 2010, 11:297-307.
109. Bhalla US. Understanding complex signaling networks through models and metaphors. Prog Biophys Mol Biol 2003, 81:45-65.
110. Oda K, Kitano H. A comprehensive map of the Tolllike receptor signaling network. Mol Syst Biol 2006, 2:2006.0015.
111. Kelder T, et al. WikiPathways: building research communities on biological pathways. Nucleic Acids Res 2011
112. Matthews L, et al. Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 2009, 37(Database issue):D619-D622.
113. Pryciak PM. Designing new cellular signaling pathways. Chem Biol 2009, 16:249-254.
114. Antunes MS, et al. Engineering key components in a synthetic eukaryotic signal transduction pathway. Mol Syst Biol 2009, 5:270.
115. Nevozhay D, et al. Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression. Proc Natl Acad Sci USA 2009, 106:5123-5128.
116. Duarte NC, et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA 2007, 104:1777-1782.
117. Herrgard MJ, et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 2008, 26:1155-1160.
118. Li F, et al. Identification of potential pathway mediation targets in Toll-like receptor signaling. PLoS Comput Biol 2009, 5:e1000292.
119. Klamt S, et al. A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinformatics 2006, 7:56.
120. Papin JA, Palsson BO. Topological analysis of massbalanced signaling networks: a framework to obtain network properties including crosstalk. J Theor Biol 2004, 227:283-297.
121. Oliveira AP, Patil KR, Nielsen J. Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks. BMC Syst Biol 2008, 2:17.
122. Franke R, et al. Host-pathogen systems biology: logical modelling of hepatocyte growth factor and Helicobacter pylori induced c-Met signal transduction. BMC Syst Biol 2008, 2:4.
123. Milo R, et al. Superfamilies of evolved and designed networks. Science 2004, 303:1538-1542.
124. Milo R, et al. Network motifs: simple building blocks of complex networks. Science 2002, 298:824-827.
125. Cook SA. The complexity of theorem-proving procedures. In: Proceedings of the 3rd Annual ACM Symposium on Theory of Computing. Assosiation for Computing Machinery; 1971, 151-158.
126. Barabasi AL, Albert R. Emergence of scaling in random networks. Science 1999, 286:509-512.
127. Wagner A. How the global structure of protein interaction networks evolves. Proc Biol Sci 2003, 270:457-466.
128. Yook SH, Oltvai ZN, Barabasi AL. Functional and topological characterization of protein interaction networks. Proteomics 2004, 4:928-942.
129. Tanaka R, Yi TM, Doyle J. Some protein interaction data do not exhibit power law statistics. FEBS Lett 2005, 579:5140-5144.
130. Abraham F, Frieze A, Vera J. A geometric preferential attachment model of networks. Internet Math 2006, 3.
131. Watts DJ, Strogatz SH. Collective dynamics of 'smallworld' networks. Nature 1998, 393:440-442.
132. Przulj N, Corneil DG, Jurisica I. Modeling interactome: scale-free or geometric? Bioinformatics 2004, 20:3508-3515.
133. Newman MEJ. The structure and function of complex networks. SIAM Rev 2003, 45:167-256.
134. Przulj N, Corneil DG, Jurisica I. Efficient estimation of graphlet frequency distributions in protein-protein interaction networks. Bioinformatics 2006, 22:974-80.
135. He X, Zhang J. Why do hubs tend to be essential in protein networks? PLoS Genet 2006, 2:e88.
136. Jeong H, et al. Lethality and centrality in protein networks. Nature 2001, 411:41-42.
137. Zotenko E, et al. Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality. PLoS Comput Biol 2008, 4:e1000140.
138. Aragues R, Sander C, Oliva B. Predicting cancer involvement of genes from heterogeneous data. BMC Bioinformatics 2008, 9:172.
139. Radivojac P, et al. An integrated approach to inferring gene-disease associations in humans. Proteins 2008, 72:1030-1037.
140. Bertin N, et al. Confirmation of organized modularity in the yeast interactome. PLoS Biol 2007, 5: e153.
141. Han JD, et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 2004, 430:88-93.
142. Batada NN, et al. Stratus not altocumulus: a new view of the yeast protein interaction network. PLoS Biol 2006, 4:e317.
143. Batada NN, et al. Still stratus not altocumulus: further evidence against the date/party hub distinction. PLoS Biol 2007, 5:e154.
144. Tsai CJ, Ma B, Nussinov R. Protein-protein interaction networks: how can a hub protein bind so many different partners? Trends Biochem Sci 2009, 34:594-600.
145. Milenkovic T, Przulj N. Uncovering biological network function via graphlet degree signatures. Cancer Inform 2008, 6:257-273.
146. Ho H, et al. Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets. BMC Syst Biol 2010, 4:84.
147. Milenkovic T, et al. Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data. J R Soc Interface 2009, 7:423-437.
148. Pieroni E, et al. Protein networking: insights into global functional organization of proteomes. Proteomics 2008, 8:799-816.
149. Estrada E. Generalized walks-based centrality measures for complex biological networks. J Theor Biol 2010, 263:556-565.
150. Goh KI, et al. The human disease network. Proc Natl Acad Sci USA 2007, 104:8685-8690.
151. Jonsson PF, Bates PA. Global topological features of cancer proteins in the human interactome. Bioinformatics 2006, 22:2291-2297.
152. Sun J, Zhao Z. A comparative study of cancer proteins in the human protein-protein interaction network. BMC Genomics 2010, 11(Suppl 3):S5.
153. Yildirim MA, et al. Drug-target network. Nat Biotechnol 2007, 25:1119-1126.
154. Lappe M, Holm L. Unraveling protein interaction networks with near-optimal efficiency. Nat Biotechnol 2004, 22:98-103.
155. Kuchaiev O, et al. Geometric de-noising of protein-protein interaction networks. PLoS Comput Biol 2009, 5:e1000454.
156. Erdos P, Renyi A. On random graphs. Publ Math 1959, 6290-297.
157. David-Eden H, Mandel-Gutfreund Y. Revealing unique properties of the ribosome using a network based analysis. Nucleic Acids Res 2008, 36:4641-4652.
158. Higham DJ, Rasajski M, Przulj N. Fitting a geometric graph to a protein-protein interaction network. Bioinformatics 2008, 24:1093-1099.
159. Przulj N. Biological network comparison using graphlet degree distribution. Bioinformatics 2007, 23:e177-e183.
160. Ivanic J, Wallqvist A, Reifman J. Evidence of probabilistic behaviour in protein interaction networks. BMC Syst Biol 2008, 2:11.
161. Ivanic J, Wallqvist A, Reifman J. Probing the extent of randomness in protein interaction networks. PLoS Comput Biol 2008, 4:e1000114.
162. Przulj N, Higham DJ. Modelling protein-protein interaction networks via a stickiness index. J R Soc Interface 2006, 3:711-716.
163. Przulj N, et al. Geometric evolutionary dynamics of protein interaction networks. In: Proceedings of the 2010 Pacific Symposium on Biocomputing. 2010.
164. Vazquez A, et al. Modeling of protein interaction networks. ComPlexUs 2001, 38-44.
165. Price DJdS. Networks of scientific papers. Science 1965, 510-515.
166. Keller EF. Revisiting "scale-free" networks. Bioessays 2005, 27:1060-1068.
167. Flannick J, et al. Graemlin: general and robust alignment of multiple large interaction networks. Genome Res 2006, 16:1169-1181.
168. Kuchaiev O, Przulj N. Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics 2011, 27:1390-136.
169. Berg J, Lassig M. Local graph alignment and motif search in biological networks. Proc Natl Acad Sci USA 2004, 101:14689-14694.
170. Berg J, Lassig M. Cross-species analysis of biological networks by Bayesian alignment. Proc Natl Acad Sci USA 2006, 103:10967-10972.
171. Flannick J, et al. Automatic parameter learning for multiple network alignment. RECOMB 2010, 214-231.
172. Kalaev M, et al. NetworkBLAST: comparative analysis of protein networks. Bioinformatics 2008, 24:594-596.
173. Kelley BP, et al. PathBLAST: a tool for alignment of protein interaction networks. Nucleic Acids Res 2004, 32(Web Server issue):W83-W88.
174. Koyuturk M, et al. Pairwise alignment of protein interaction networks. J Comput Biol 2006, 13:182-199.
175. Liang Z, et al. NetAlign: a Web-based tool for comparison of protein interaction networks. Bioinformatics 2006, 22:2175-2177.
176. Liang Z, et al. Comparison of protein interaction networks reveals species conservation and divergence. BMC Bioinformatics 2006, 7:457.
177. Liao CS, et al. IsoRankN: spectral methods for global alignment of multiple protein networks. Bioinformatics 2009, 25:i253-i258.
178. Singh R, Xu J, Berger B. Pairwise global alignment of protein interaction networks by matching neighborhood topology. Res Comput Mol Biol 2007:16-31.
179. Singh R, Xu J, Berger B. Global alignment of multiple protein interaction networks 2008. Proc Pacific Symp Biocomput 2008:303-314.
180. Kuchaiev O, et al. Topological network alignment uncovers biological function and phylogeny. J R Soc Interface 2010, 7:1341-1354.
181. Sharan R, Ulitsky I, Shamir R. Network-based prediction of protein function. Mol Syst Biol 2007, 3:88.
182. 2 conditions and salinity stress on Arabidopsis thaliana liquid cultures: comparing the early molecular response using time-series transcriptomic and metabolomic analyses. BMC Syst Biol 2010, 4:177.
183. Beyer A, Bandyopadhyay S, Ideker T. Integrating physical and genetic maps: from genomes to interaction networks. Nat Rev Genet 2007, 8:699-710.
184. Newman JR, Weissman JS. Systems biology: many things from one. Nature 2006, 444:561-562.
185. Fong SS, et al. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol Bioeng 2005, 91:643-648.
186. Blank LM, Lehmbeck F, Sauer U. Metabolic-flux and network analysis in fourteen hemiascomycetous yeasts. FEMS Yeast Res 2005, 5:545-558.
187. Chavali AK, et al. Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major. Mol Syst Biol 2008, 4:177.
188. Zomorrodi AR, Maranas CD. Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data. BMC Syst Biol 4:178.
189. Pharkya P, Burgard AP, Maranas CD. Exploring the overproduction of amino acids using the bilevel optimization framework OptKnock. Biotechnol Bioeng 2003, 84:887-899.
190. Christensen B, Nielsen J. Metabolic network analysis. A powerful tool in metabolic engineering. Adv Biochem Eng Biotechnol 2000, 66:209-231.
191. Wiechert W. 13C metabolic flux analysis. Metab Eng 2001, 3:195-206.
192. Fischer E, Sauer U. Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism. Nat Genet 2005, 37:636-640.
193. Haverkorn van Rijsewijk BR, et al. Large-scale 13Cflux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli. Mol Syst Biol 2011, 7:477.
194. Wahl SA, Noh K, Wiechert W. 13C labeling experiments at metabolic nonstationary conditions: an exploratory study. BMC Bioinformatics 2008, 9:152.
195. Niklas J, Heinzle E. Metabolic flux analysis in systems biology of mammalian cells. Adv Biochem Eng Biotechnol 2007.
196. Jamshidi N, Palsson BO. Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models. Biophys J 2010, 98:175-185.
197. Becker SA, Palsson BO. Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 2008, 4:e1000082.
198. Shlomi T, et al. Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 2008, 26:1003-1010.
199. Oberhardt MA, et al. Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection. J Bacteriol 2010, 192:5534-5548.
200. Colijn C, et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 2009, 5:e1000489.
201. Bar-Yam Y, Harmon D, de Bivort B. Systems biology. Attractors and democratic dynamics. Science 2009, 323:1016-1017.
202. Oti M, Brunner HG. The modular nature of genetic diseases. Clin Genet 2007, 71:1-11.
203. Titz B, Schlesner M, Uetz P. What do we learn from high-throughput protein interaction data? Expert Rev Proteomics 2004, 1:111-121.
204. Lage K, et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 2007, 25:309-316.
205. Amberger J, et al. McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res 2009, 37(Database issue):D793-D796.
206. Hamosh A, et al. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 2005, 33(Database issue):D514-D517.
207. Raman K, Chandra N. Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiol 2008, 8:234.
208. Lee SH, et al. Antagonism of chemical genetic interaction networks resensitize MRSA to beta-lactam antibiotics. Chem Biol 2011, 18:1379-1389.
209. Goltsov A, et al. Features of the reversible sensitivityresistance transition in PI3K/PTEN/AKT signalling network after HER2 inhibition. Cell Signal 2012, 24:493-504.
210. Chao SY, et al. An integrative approach to identifying cancer chemoresistance-associated pathways. BMC Med Genomics 2011, 4:23.
211. Chavez JD, et al. Quantitative proteomic and interaction network analysis of cisplatin resistance in HeLa cells. PLoS One 2011, 6:e19892.
212. Assenov Y, et al. Computing topological parameters of biological networks. Bioinformatics 2008, 24:282-284.
213. Audenaert P, et al. CyClus3D: a Cytoscape plugin for clustering network motifs in integrated networks. Bioinformatics 2011, 27:1587-1588.
214. Kuchaiev O, et al. GraphCrunch 2: software tool for network modeling, alignment and clustering. BMC Bioinformatics 2011, 12:24.
215. Carey VJ, et al. Network structures and algorithms in bioconductor. Bioinformatics 2005, 21:135-136.
216. Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 2005, 21:3448-3449.
217. Merico D, Gfeller D, Bader GD. Howto visually interpret biological data using networks. Nat Biotechnol 2009, 27:921-924.
218. Cline MS, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2007, 2:2366-2382.
219. Baitaluk M, et al. BiologicalNetworks: visualization and analysis tool for systems biology. Nucleic Acids Res 2006, 34(Web Server issue):W466-W471.
220. Hu Z, et al. VisANT: an online visualization and analysis tool for biological interaction data. BMC Bioinformatics 2004, 5:17.
221. Yip KY, et al. The tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks. Bioinformatics 2006, 22:2968-2970.
222. Kashtan N, et al. Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 2004, 20:1746-1758.
223. Smoot ME, et al. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2010, 27:431-432.
224. Dahlquist KD, et al. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet 2002, 31:19-20.
225. Breitkreutz BJ, Stark C, Tyers M. Osprey: a network visualization system. Genome Biol 2003, 4:R22.
226. Van Hemert JL, Dickerson JA. PathwayAccess: CellDesigner plugins for pathway databases. Bioinformatics 2010, 26:2345-2346.
227. Nikitin A, et al. Pathway studio-the analysis and navigation of molecular networks. Bioinformatics 2003, 19:2155-2157.
228. Beisser D, et al. BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics 2010, 26:1129-1130.
229. Cornell M, Paton NW, Oliver SG. A critical and integrated view of the yeast interactome. Comput Funct Genomics 2004, 5:382-402.
230. Wodak SJ, et al. Challenges and rewards of interaction proteomics. Mol Cell Proteomics 2009, 8:3-18.
231. Ivanic J, et al. Influence of protein abundance on high-throughput protein-protein interaction detection. PLoS One 2009, 4:e5815.
232. Edwards AM, et al. Bridging structural biology and genomics: assessing protein interaction data with known complexes. Trends Genet 2002, 18:529-536.
233. Ideker T, Dutkowski J, Hood L. Boosting signal-tonoise in complex biology: prior knowledge is power. Cell 2011, 144:860-863.
234. Vaquerizas JM, et al. A census of human transcription factors: function, expression and evolution. Nat Rev Genet 2009, 10:252-263.
235. Cusick ME, et al. Interactome: gateway into systems biology. Hum Mol Genet 2005, 14(Spec No. 2):R171-R181.
236. Hart GT, Ramani AK, Marcotte EM. How complete are current yeast and human protein-interaction networks? Genome Biol 2006, 7:120.
237. Sambourg L, Thierry-Mieg N. New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size. BMC Bioinformatics 2010, 11:605.
238. Stumpf MP, et al. Estimating the size of the human interactome. Proc Natl Acad Sci USA 2008, 105:6959-6964.
239. Stumpf MP, Wiuf C, May RM. Subnets of scale-free networks are not scale-free: sampling properties of networks. Proc Natl Acad Sci USA 2005, 102:4221-4224.
240. Han JD, et al. Effect of sampling on topology predictions of protein-protein interaction networks. Nat Biotechnol 2005, 23:839-844.
241. Ma'ayan A. Introduction to network analysis in systems biology. Sci Signal 2011, 4:tr5.
242. Pe'er D, Hacohen N. Principles and strategies for developing network models in cancer. Cell 2011, 144:864-873.
243. Sachs K, et al. Causal protein-signaling networks derived from multiparameter single-cell data. Science 2005, 308:523-529.
244. Meyer P, et al. Verification of systems biology research in the age of collaborative competition. Nat Biotechnol 2011, 29:811-815.
245. Dutta B, Snyder R, Klapa MI. Significance analysis of time-series transcriptomic data: a methodology that enables the identification and further exploration of the differentially expressed genes at each time-point. Biotechnol Bioeng 2007, 98:668-678.
246. Lee JM, et al. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput Biol 2008, 4:e1000086.
247. Herrgard MJ, et al. Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res 2006, 16:627-635.
248. Hede K. Superhighway or blind alley? The cancer genome atlas releases first results. J Natl Cancer Inst 2008, 100:1566-1569.