Computational approaches for analyzing information flow in biological networks

Science Signaling

Volumn 5, Issue 220, 2012, Pages

  Kholodenko, Boris ^a   Yaffe, Michael B ^b   Kolch, Walter ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; FOCAL ADHESION KINASE; GUANOSINE TRIPHOSPHATASE; IMMUNOGLOBULIN ENHANCER BINDING PROTEIN; MAMMALIAN TARGET OF RAPAMYCIN COMPLEX 2; MESSENGER RNA; PROTEIN C FOS; RAF PROTEIN; STRESS ACTIVATED PROTEIN KINASE; TRANSCRIPTION FACTOR; TRANSCRIPTOME; WNT PROTEIN;

APOPTOSIS; BIOLOGICAL FUNCTIONS; CELL COMMUNICATION AND CELL CONTACT; CELL CYCLE G1 PHASE; CELL CYCLE S PHASE; CELL DEATH; CELL DIFFERENTIATION; CELL INTERACTION; CELL MIGRATION; CELLULAR, SUBCELLULAR AND MOLECULAR BIOLOGICAL PHENOMENA AND FUNCTIONS; COMPUTER MODEL; DNA DAMAGE; DRUG TARGETING; ENZYME LINKED IMMUNOSORBENT ASSAY; FUZZY LOGIC; GENETIC REGULATION; GENOMICS; HIGH THROUGHPUT SEQUENCING; HUMAN; IMMUNOBLOTTING; INFORMATION SCIENCE; LIPIDOMICS; MASS SPECTROMETRY; MATHEMATICAL MODEL; METABOLOMICS; NONHUMAN; ONCOGENE C FOS; OSMOTIC STRESS; POISSON DISTRIBUTION; PRIORITY JOURNAL; PROMOTER REGION; PROTEIN ANALYSIS; PROTEIN DOMAIN; PROTEIN MICROARRAY; PROTEIN MODIFICATION; PROTEIN PHOSPHORYLATION; PROTEIN PROCESSING; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; PROTEOMICS; REGRESSION ANALYSIS; REVIEW; RNA INTERFERENCE; SENSITIVITY ANALYSIS; SIGNAL TRANSDUCTION; STEM CELL; STOCHASTIC MODEL; TWO HYBRID SYSTEM; WNT SIGNALING PATHWAY; ARTICLE; BIOLOGY; COMPUTER SIMULATION; METHODOLOGY; SYSTEMS BIOLOGY;

COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; PROTEOMICS; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

EID: 84859994220     PISSN: 19450877     EISSN: 19379145     Source Type: Journal    
DOI: 10.1126/scisignal.2002961     Document Type: Review

References (172)

1. M. Vidal, M. E. Cusick, A. L. Barabási, Interactome networks and human disease. Cell 144, 986-998 (2011).
2. K. Venkatesan, J. F. Rual, A. Vazquez, U. Stelzl, I. Lemmens, T. Hirozane-Kishikawa, T. Hao, M. Zenkner, X. Xin, K. I. Goh, M. A. Yildirim, N. Simonis, K. Heinzmann, F. Gebreab, J. M. Sahalie, S. Cevik, C. Simon, A. S. de Smet, E. Dann, A. Smolyar, A. Vinayagam, H. Yu, D. Szeto, H. Borick, A. Dricot, N. Klitgord, R. R. Murray, C. Lin, M. Lalowski, J. Timm, K. Rau, C. Boone, P. Braun, M. E. Cusick, F. P. Roth, D. E. Hill, J. Tavernier, E. E. Wanker, A. L. Barabási, M. Vidal, An empirical framework for binary interactome mapping. Nat. Methods 6, 83-90 (2009).
3. S. Bandyopadhyay, C. Y. Chiang, J. Srivastava, M. Gersten, S. White, R. Bell, C. Kurschner, C. H. Martin, M. Smoot, S. Sahasrabudhe, D. L. Barber, S. K. Chanda, T. Ideker, A human MAP kinase interactome. Nat. Methods 7, 801-805 (2010).
4. A. Vinayagam, U. Stelzl, R. Foulle, S. Plassmann, M. Zenkner, J. Timm, H. E. Assmus, M. A. An-drade- Navarro, E. E. Wanker, A directed protein interaction network for investigating intracellular signal transduction. Sci. Signal. 4, rs8 (2011).
5. J. S. Andersen, C. J. Wilkinson, T. Mayor, P. Mortensen, E. A. Nigg, M. Mann, Proteomic characterization of the human centrosome by protein correlation profiling. Nature 426, 570-574 (2003).
6. H. Müller, D. Schmidt, S. Steinbrink, E. Mirgorodskaya, V. Lehmann, K. Habermann, F. Dreher, N. Gustavsson, T. Kessler, H. Lehrach, R. Herwig, J. Gobom, A. Ploubidou, M. Boutros, B. M. Lange, Proteomic and functional analysis of the mitotic Drosophila centrosome. EMBO J. 29, 3344-3357 (2010).
7. A. Santamaria, B. Wang, S. Elowe, R. Malik, F. Zhang, M. Bauer, A. Schmidt, H. H. Silljé, R. Körner, E. A. Nigg, The Plk1-dependent phosphoproteome of the early mitotic spindle. Mol. Cell. Proteomics 10, M110.004457 (2011).
8. M. K. Bonner, D. S. Poole, T. Xu, A. Sarkeshik, J. R. Yates, 3rd, A. R. Skop, Mitotic spindle proteomics in Chinese hamster ovary cells. PLoS ONE 6, e20489 (2011).
9. K. Grosstessner-Hain, B. Hegemann, M. Novatchkova, J. Rameseder, B. A. Joughin, O. Hudecz, E. Roitinger, P. Pichler, N. Kraut, M. B. Yaffe, J. M. Peters, K. Mechtler, Quantitative phospho-proteomics to investigate the polo-like kinase 1-dependent phospho-proteome. Mol. Cell. Proteomics 10, M111.008540 (2011).
10. G. Sauer, R. Körner, A. Hanisch, A. Ries, E. A. Nigg, H. H. Silljé, Proteome analysis of the human mitotic spindle. Mol. Cell. Proteomics 4, 35-43 (2005).
11. S. Ohta, J. C. Bukowski-Wills, L. Sanchez-Pulido, F. L. Alves, L. Wood, Z. A. Chen, M. Platani, L. Fischer, D. F. Hudson, C. P. Ponting, T. Fukagawa, W. C. Earnshaw, J. Rappsilber, The protein composition of mitotic chromosomes determined using multiclassifier combinatorial proteomics. Cell 142, 810-821 (2010).
12. K. Boldt, J. van Reeuwijk, C. J. Gloeckner, M. Ueffi ng, R. Roepman, Tandem affinity purification of ciliopathy-associated protein complexes. Methods Cell Biol. 91, 143-160 (2009).
13. G. Rigaut, A. Shevchenko, B. Rutz, M. Wilm, M. Mann, B. Séraphin, A generic protein purification method for protein complex characterization and proteome exploration. Nat. Biotechnol. 17, 1030- 1032 (1999).
14. J. Dengjel, V. Akimov, J. V. Olsen, J. Bunkenborg, M. Mann, B. Blagoev, J. S. Andersen, Quantitative proteomic assessment of very early cellular signaling events. Nat. Biotechnol. 25, 566-568 (2007).
15. A. von Kriegsheim, D. Baiocchi, M. Birtwistle, D. Sumpton, W. Bienvenut, N. Morrice, K. Yamada, A. Lamond, G. Kalna, R. Orton, D. Gilbert, W. Kolch, Cell fate decisions are specified by the dynamic ERK interactome. Nat. Cell Biol. 11, 1458-1464 (2009).
16. W. Kolch, A. Pitt, Functional proteomics to dissect tyrosine kinase signalling pathways in cancer. Nat. Rev. Cancer 10, 618-629 (2010).
17. B. Blagoev, I. Kratchmarova, S. E. Ong, M. Nielsen, L. J. Foster, M. Mann, A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling. Nat. Biotechnol. 21, 315-318 (2003).
18. A. Wepf, T. Glatter, A. Schmidt, R. Aebersold, M. Gstaiger, Quantitative interaction proteomics using mass spectrometry. Nat. Methods 6, 203-205 (2009).
19. T. Bouwmeester, A. Bauch, H. Ruffner, P. O. Angrand, G. Bergamini, K. Croughton, C. Cruciat, D. Eberhard, J. Gagneur, S. Ghidelli, C. Hopf, B. Huhse, R. Mangano, A. M. Michon, M. Schirle, J. Schlegl, M. Schwab, M. A. Stein, A. Bauer, G. Casari, G. Drewes, A. C. Gavin, D. B. Jackson, G. Joberty, G. Neubauer, J. Rick, B. Kuster, G. Super-ti-Furga, A physical and functional map of the human TNF-alpha/NF-kappa B signal transduction pathway. Nat. Cell Biol. 6, 97-105 (2004).
20. N. J. Krogan, G. Cagney, H. Yu, G. Zhong, X. Guo, A. Ignatchenko, J. Li, S. Pu, N. Datta, A. P. Tikuisis, T. Punna, J. M. Peregrín-Alvarez, M. Shales, X. Zhang, M. Davey, M. D. Robinson, A. Paccanaro, J. E. Bray, A. Sheung, B. Beattie, D. P. Richards, V. Canadien, A. Lalev, F. Mena, P. Wong, A. Starostine, M. M. Canete, J. Vlasblom, S. Wu, C. Orsi, S. R. Collins, S. Chandran, R. Haw, J. J. Rilstone, K. Gandi, N. J. Thompson, G. Musso, P. St Onge, S. Ghanny, M. H. Lam, G. Butland, A. M. Altaf-Ul, S. Kanaya, A. Shilatifard, E. O'Shea, J. S. Weissman, C. J. Ingles, T. R. Hughes, J. Parkinson, M. Gerstein, S. J. Wodak, A. Emili, J. F. Greenblatt, Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637-643 (2006).
21. A. C. Gavin, P. Aloy, P. Grandi, R. Krause, M. Boesche, M. Marzioch, C. Rau, L. J. Jensen, S. Bastuck, B. Dümpelfeld, A. Edelmann, M. A. Heurtier, V. Hoffman, C. Hoefert, K. Klein, M. Hudak, A. M. Michon, M. Schelder, M. Schirle, M. Remor, T. Rudi, S. Hooper, A. Bauer, T. Bouwmeester, G. Casari, G. Drewes, G. Neubauer, J. M. Rick, B. Kuster, P. Bork, R. B. Russell, G. Superti-Furga, Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631-636 (2006).
22. K. G. Guruharsha, J. F. Rual, B. Zhai, J. Mintseris, P. Vaidya, N. Vaidya, C. Beekman, C. Wong, D. Y. Rhee, O. Cenaj, E. McKillip, S. Shah, M. Stapleton, K. H. Wan, C. Yu, B. Parsa, J. W. Carlson, X. Chen, B. Kapadia, K. VijayRaghavan, S. P. Gygi, S. E. Celniker, R. A. Obar, S. Artavanis-Tsakonas, A protein complex network of Drosophila melanogaster. Cell 147, 690-703 (2011).
23. L. Kaderali, E. Dazert, U. Zeuge, M. Frese, R. Bartenschlager, Reconstructing signaling pathways from RNAi data using probabilistic Boolean threshold networks. Bioinformatics 25, 2229-2235 (2009).
24. H. Froehlich, M. Fellmann, H. Sueltmann, A. Poustka, T. Beissbarth, Large scale statistical inference of signaling pathways from RNAi and microarray data. BMC Bioinformatics 8, 386 (2007).
25. J. Hitomi, D. E. Christofferson, A. Ng, J. Yao, A. Degterev, R. J. Xavier, J. Yuan, Identification of a molecular signaling network that regulates a cellular necrotic cell death pathway. Cell 135, 1311-1323 (2008).
26. A. Saj, Z. Arziman, D. Stempfle, W. van Belle, U. Sauder, T. Horn, M. Dürrenberger, R. Paro, M. Boutros, G. Merdes, A combined ex vivo and in vivo RNAi screen for notch regulators in Drosophila reveals an extensive notch interaction network. Dev. Cell 18, 862-876 (2010).
27. S. Kondo, N. Perrimon, A genome-wide RNAi screen identifies core components of the G2-M DNA damage checkpoint. Sci. Signal. 4, rs1 (2011).
28. X. D. Zhang, F. Santini, R. Lacson, S. D. Marine, Q. Wu, L. Benetti, R. Yang, A. McCampbell, J. P. Berger, D. M. Toolan, E. M. Stec, D. J. Holder, K. A. Soper, J. F. Heyse, M. Ferrer, cSSMD: assessing collective activity for addressing off-target effects in genome-scale RNA interference screens. Bioinformatics 27, 2775-2781 (2011).
29. G. D. Bader, I. Donaldson, C. Wolting, B. F. Ouellette, T. Pawson, C. W. Hogue, BIND - The Biomolecular Interaction Network Database. Nucleic Acids Res. 29, 242-245 (2001).
30. C. Stark, B. J. Breitkreutz, T. Reguly, L. Boucher, A. Breitkreutz, M. Tyers, BioGRID: A general repository for interaction datasets. Nucleic Acids Res. 34 (Database issue), D535-D539 (2006).
31. A. Chatr-aryamontri, A. Ceol, L. M. Palazzi, G. Nardelli, M. V. Schneider, L. Castagnoli, G. Cesareni, MINT: the Molecular INTeraction database. Nucleic Acids Res. 35 (Database issue), D572-D574 (2007).
32. I. Xenarios, D. W. Rice, L. Salwinski, M. K. Baron, E. M. Marcotte, D. Eisenberg, DIP: The database of interacting proteins. Nucleic Acids Res. 28, 289-291 (2000).
33. D. Szklarczyk, A. Franceschini, M. Kuhn, M. Simonovic, A. Roth, P. Minguez, T. Doerks, M. Stark, J. Muller, P. Bork, L. J. Jensen, C. von Mering, The STRING database in 2011: Functional inter action networks of proteins, globally integrated and scored. Nucleic Acids Res. 39 (Database issue), D561-D568 (2011).
34. B. Turner, S. Razick, A. L. Turinsky, J. Vlasblom, E. K. Crowdy, E. Cho, K. Morrison, I. M. Donaldson, S. J. Wodak, iRefWeb: Interactive analysis of consolidated protein interaction data and their supporting evidence. Database (Oxford) 2010, baq023 (2010).
35. M. Kanehisa, S. Goto, Y. Sato, M. Furumichi, M. Tanabe, KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40 (Database issue), D109-D114 (2012).
36. L. Matthews, G. Gopinath, M. Gillespie, M. Caudy, D. Croft, B. de Bono, P. Garapati, J. Hemish, H. Hermjakob, B. Jassal, A. Kanapin, S. Lewis, S. Mahajan, B. May, E. Schmidt, I. Vastrik, G. Wu, E. Birney, L. Stein, P. D'Eustachio, Reactome knowledge-base of human biological pathways and processes. Nucleic Acids Res. 37 (Database issue), D619-D622 (2009).
37. D. Croft, G. O'Kelly, G. Wu, R. Haw, M. Gillespie, L. Matthews, M. Caudy, P. Garapati, G. Gopinath, B. Jassal, S. Jupe, I. Kalatskaya, S. Mahajan, B. May, N. Ndegwa, E. Schmidt, V. Shamovsky, C. Yung, E. Birney, H. Hermjakob, P. D'Eustachio, L. Stein, Reactome: A database of reactions, pathways and biological processes. Nucleic Acids Res. 39 (Database issue), D691-D697 (2011).
38. E. G. Cerami, B. E. Gross, E. Demir, I. Rodchenkov, O. Babur, N. Anwar, N. Schultz, G. D. Bader, C. Sander, Pathway Commons, a Web resource for biological pathway data. Nucleic Acids Res. 39 (Database issue), D685-D690 (2011).
39. C. F. Schaefer, K. Anthony, S. Krupa, J. Buchoff, M. Day, T. Hannay, K. H. Buetow, PID: The Pathway Interaction Database. Nucleic Acids Res. 37 (Database issue), D674-D679 (2009).
40. C. Li, M. Donizelli, N. Rodriguez, H. Dharuri, L. Endler, V. Chelliah, L. Li, E. He, A. Henry, M. I. Stefan, J. L. Snoep, M. Hucka, N. Le Novère, C. Laibe, BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst. Biol. 4, 92 (2010).
41. S. Matsuoka, B. A. Ballif, A. Smogorzewska, E. R. McDonald, 3rd, K. E. Hurov, J. Luo, C. E. Bakalarski, Z. Zhao, N. Solimini, Y. Lerenthal, Y. Shiloh, S. P. Gygi, S. J. Elledge, ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage. Science 316, 1160-1166 (2007).
42. A. Bensimon, A. Schmidt, Y. Ziv, R. Elkon, S. Y. Wang, D. J. Chen, R. Aebersold, Y. Shiloh, ATM-dependent and -independent dynamics of the nuclear phosphoproteome after DNA damage. Sci. Signal. 3, rs3 (2010).
43. K. T. Rigbolt, T. A. Prokhorova, V. Akimov, J. Henningsen, P. T. Johansen, I. Kratchmarova, M. Kassem, M. Mann, J. V. Olsen, B. Blagoev, Systemwide temporal characterization of the proteome and phosphoproteome of human embryonic stem cell differentiation. Sci. Signal. 4, rs3 (2011).
44. N. Mischerikow, A. J. Heck, Targeted large-scale analysis of protein acetylation. Proteomics 11, 571-589 (2011).
45. B. T. Weinert, S. A. Wagner, H. Horn, P. Henriksen, W. R. Liu, J. V. Olsen, L. J. Jensen, C. Choudhary, Proteome-wide mapping of the Drosophila acetylome demonstrates a high degree of conservation of lysine acetylation. Sci. Signal. 4, ra48 (2011).
46. S. A. Wagner, P. Beli, B. T. Weinert, M. L. Nielsen, J. Cox, M. Mann, C. Choudhary, A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles. Mol. Cell. Proteomics 10, M111.013284 (2011).
47. C. Choudhary, M. Mann, Decoding signalling networks by mass spectrometry-based proteomics. Nat. Rev. Mol. Cell Biol. 11, 427-439 (2010).
48. K. A. Janes, J. G. Albeck, L. X. Peng, P. K. Sorger, D. A. Lauffenburger, M. B. Yaffe, A high-throughput quantitative multiplex kinase assay for monitoring information flow in signaling networks: Application to sepsis-apoptosis. Mol. Cell. Proteomics 2, 463-473 (2003).
49. C. Bender, F. Henjes, H. Fröhlich, S. Wiemann, U. Korf, T. Beissbarth, Dynamic deterministic effects propagation networks: Learning signalling pathways from longitudinal protein array data. Bioinformatics 26, i596-i602 (2010).
50. K. Malinowsky, C. Wolff, B. Ergin, D. Berg, K. F. Becker, Deciphering signaling pathways in clinical tissues for personalized medicine using protein microarrays. J. Cell. Physiol. 225, 364-370 (2010).
51. S. Gaudet, K. A. Janes, J. G. Albeck, E. A. Pace, D. A. Lauffenburger, P. K. Sorger, A compendium of signals and responses triggered by prodeath and prosurvival cytokines. Mol. Cell. Proteomics 4, 1569-1590 (2005).
52. E. O'Neill, L. Rushworth, M. Baccarini, W. Kolch, Role of the kinase MST2 in suppression of apoptosis by the proto-oncogene product Raf-1. Science 306, 2267-2270 (2004).
53. D. Matallanas, D. Romano, K. Yee, K. Meissl, L. Kucerova, D. Piazzolla, M. Baccarini, J. K. Vass, W. Kolch, E. O'neill, RASSF1A elicits apoptosis through an MST2 pathway directing proapoptotic transcription by the p73 tumor suppressor protein. Mol. Cell 27, 962-975 (2007).
54. K. A. Janes, M. B. Yaffe, Data-driven modelling of signal-transduction networks. Nat. Rev. Mol. Cell Biol. 7, 820-828 (2006).
55. Y. Zhang, A. Wolf-Yadlin, P. L. Ross, D. J. Pappin, J. Rush, D. A. Lauffenburger, F. M. White, Time-resolved mass spectrometry of tyrosine phosphorylation sites in the epidermal growth factor receptor signaling network reveals dynamic modules. Mol. Cell. Proteomics 4, 1240-1250 (2005).
56. M. Krüger, I. Kratchmarova, B. Blagoev, Y. H. Tseng, C. R. Kahn, M. Mann, Dissection of the insulin signaling pathway via quantitative phosphoproteomics. Proc. Natl. Acad. Sci. U.S.A. 105, 2451-2456 (2008).
57. J. Jin, F. D. Smith, C. Stark, C. D. Wells, J. P. Fawcett, S. Kulkarni, P. Metalnikov, P. O'Donnell, P. Taylor, L. Taylor, A. Zougman, J. R. Woodgett, L. K. Langeberg, J. D. Scott, T. Pawson, Proteomic, functional, and domain-based analysis of in vivo 14-3-3 binding proteins involved in cytoskeletal regulation and cellular organization. Curr. Biol. 14, 1436-1450 (2004).
58. D. M. Lowery, K. R. Clauser, M. Hjerrild, D. Lim, J. Alexander, K. Kishi, S. E. Ong, S. Gammeltoft, S. A. Carr, M. B. Yaffe, Proteomic screen defines the Po-lo-box domain interactome and identifies Rock2 as a Plk1 substrate. EMBO J. 26, 2262-2273 (2007).
59. E. W. Wilker, M. A. van Vugt, S. A. Artim, P. H. Huang, C. P. Petersen, H. C. Reinhardt, Y. Feng, P. A. Sharp, N. Sonenberg, F. M. White, M. B. Yaffe, 14-3-3sigma controls mitotic translation to facilitate cytokinesis. Nature 446, 329-332 (2007).
60. F. Dubois, F. Vandermoere, A. Gernez, J. Murphy, R. Toth, S. Chen, K. M. Geraghty, N. A. Morrice, C. MacKintosh, Differential 14-3-3 affinity capture reveals new downstream targets of phosphatidylinositol 3-kinase signaling. Mol. Cell. Proteomics 8, 2487-2499 (2009).
61. J. D. Blethrow, J. S. Glavy, D. O. Morgan, K. M. Shokat, Covalent capture of kinase-specific phosphopeptides reveals Cdk1-cyclin B substrates. Proc. Natl. Acad. Sci. U.S.A. 105, 1442-1447 (2008).
62. B. Bodenmiller, S. Wanka, C. Kraft, J. Urban, D. Campbell, P. G. Pedrioli, B. Gerrits, P. Picotti, H. Lam, O. Vitek, M. Y. Brusniak, B. Roschitzki, C. Zhang, K. M. Shokat, R. Schlapbach, A. Colman- Lerner, G. P. Nolan, A. I. Nesvizhskii, M. Peter, R. Loewith, C. von Mering, R. Aebersold, Phosphoproteomic analysis reveals interconnected systemwide responses to perturbations of kinases and phosphatases in yeast. Sci. Signal. 3, rs4 (2010).
63. K. M. Naegle, M. Gymrek, B. A. Joughin, J. P. Wagner, R. E. Welsch, M. B. Yaffe, D. A. Lauffenburger, F. M. White, PTMScout, a Web resource for analysis of high throughput post-translational proteomics studies. Mol. Cell. Proteomics 9, 2558-2570 (2010).
64. K. M. Naegle, R. E. Welsch, M. B. Yaffe, F. M. White, D. A. Lauffenburger, MCAM: Multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets. PLOS Comput. Biol. 7, e1002119 (2011).
65. N. Kumar, A. Wolf-Yadlin, F. M. White, D. A. Lauffenburger, Modeling HER2 effects on cell behavior from mass spectrometry phosphotyrosine data. PLOS Comput. Biol. 3, e4 (2007).
66. K. A. Janes, H. C. Reinhardt, M. B. Yaffe, Cytokine-induced signaling networks prioritize dynamic range over signal strength. Cell 135, 343-354 (2008).
67. A. R. Tentner, M. J. Lee, G. J. Ostheimer, L. D. Samson, D. A. Lauffenburger, M. B. Yaffe, Combined experimental and computational analysis of DNA damage signaling reveals context-dependent roles for Erk in apoptosis and G1/S arrest after genotoxic stress. Mol. Syst. Biol. 8, 568 (2012).
68. G. F. Cooper, E. Herskovits, A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309-347 (1992).
69. D. Pe'er, Bayesian network analysis of signaling networks: a primer. Sci. STKE 2005, pl4 (2005).
70. K. Sachs, O. Perez, D. Pe'er, D. A. Lauffenburger, G. P. Nolan, Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523-529 (2005).
71. K. Sachs, D. Gifford, T. Jaakkola, P. Sorger, D. A. Lauffenburger, Bayesian network approach to cell signaling pathway modeling. Sci. STKE 2002, pe38 (2002).
72. S. A. Kauffman, Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22, 437-467 (1969).
73. R. Poltz, R. Franke, K. Schweitzer, S. Klamt, E. D. Gilles, M. Naumann, Logical network of genotoxic stress-induced NF-κB signal transduction predicts putative target structures for therapeutic intervention strategies. Adv. Appl. Bioinform. Chem. 2, 125-138 (2009).
74. J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. Lauffenburger, S. Klamt, P. K. Sorger, Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol. Syst. Biol. 5, 331 (2009).
75. S. Y. Shin, O. Rath, A. Zebisch, S. M. Choo, W. Kolch, K. H. Cho, Functional roles of multiple feedback loops in extracellular signal-regulated kinase and Wnt signaling pathways that regulate epithelial-mesenchymal transition. Cancer Res. 70, 6715-6724 (2010).
76. S. Hautaniemi, S. Kharait, A. Iwabu, A. Wells, D. A. Lauffenburger, Modeling of signal-response cascades using decision tree analysis. Bioinformatics 21, 2027-2035 (2005).
77. M. K. Morris, J. Saez-Rodriguez, P. K. Sorger, D. A. Lauffenburger, Logic-based models for the analysis of cell signaling networks. Biochemistry 49, 3216-3224 (2010).
78. D. T. Gillespie, Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem. 58, 35-55 (2007).
79. B. Novak, J. J. Tyson, Numerical analysis of a comprehensive model of M-phase control in Xenopus oocyte extracts and intact embryos. J. Cell Sci. 106, 1153-1168 (1993).
80. A. Csikász-Nagy, D. Battogtokh, K. C. Chen, B. Novák, J. J. Tyson, Analysis of a generic model of eukaryotic cell-cycle regulation. Biophys. J. 90, 4361-4379 (2006).
81. B. N. Kholodenko, O. V. Demin, G. Moehren, J. B. Hoek, Quantification of short term signaling by the epidermal growth factor receptor. J. Biol. Chem. 274, 30169-30181 (1999).
82. B. Schoeberl, C. Eichler-Jonsson, E. D. Gilles, G. Müller, Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat. Biotechnol. 20, 370-375 (2002).
83. A. Arkin, J. Ross, H. H. McAdams, Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 149, 1633-1648 (1998).
84. M. Thattai, A. van Oudenaarden, Intrinsic noise in gene regulatory networks. Proc. Natl. Acad. Sci. U.S.A. 98, 8614-8619 (2001).
85. T. Nakakuki, M. R. Birtwistle, Y. Saeki, N. Yumoto, K. Ide, T. Nagashima, L. Brusch, B. A. Ogunnaike, M. Okada-Hatakeyama, B. N. Kholodenko, Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. Cell 141, 884-896 (2010).
86. G. C. Brown, H. V. Westerhoff, B. N. Kholodenko, Molecular control analysis: Control within proteins and molecular processes. J. Theor. Biol. 182, 389-396 (1996).
87. L. Matthews, G. Gopinath, M. Gillespie, M. Caudy, D. Croft, B. de Bono, P. Garapati, J. Hemish, H. Hermjakob, B. Jassal, A. Kanapin, S. Lewis, S. Mahajan, B. May, E. Schmidt, I. Vastrik, G. Wu, E. Birney, L. Stein, P. D'Eustachio, Reactome knowledge-base of human biological pathways and processes. Nucleic Acids Res. 37 (Database issue), D619-D622 (2009).
88. M. R. Birtwistle, M. Hatakeyama, N. Yumoto, B. A. Ogunnaike, J. B. Hoek, B. N. Kholodenko, Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol. Syst. Biol. 3, 144 (2007).
89. N. M. Borisov, N. I. Markevich, J. B. Hoek, B. N. Kholodenko, Signaling through receptors and scaffolds: Independent interactions reduce combinatorial complexity. Biophys. J. 89, 951-966 (2005).
90. C. J. Morton-Firth, D. Bray, Predicting temporal fluctuations in an intracellular signalling pathway. J. Theor. Biol. 192, 117-128 (1998).
91. W. S. Hlavacek, J. R. Faeder, M. L. Blinov, R. G. Posner, M. Hucka, W. Fontana, Rules for modeling signal-transduction systems. Sci. STKE 2006, re6 (2006).
92. J. Feret, V. Danos, J. Krivine, R. Harmer, W. Fontana, Internal coarse-graining of molecular systems. Proc. Natl. Acad. Sci. U.S.A. 106, 6453-6458 (2009).
93. M. L. Blinov, J. R. Faeder, B. Goldstein, W. S. Hlavacek, BioNetGen: Software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20, 3289-3291 (2004).
94. L. Lok, R. Brent, Automatic generation of cellular reaction networks with Moleculizer 1.0. Nat. Biotechnol. 23, 131-136 (2005).
95. N. M. Borisov, A. S. Chistopolsky, J. R. Faeder, B. N. Kholodenko, Domain-oriented reduction of rule-based network models. IET Syst. Biol. 2, 342-351 (2008).
96. M. W. Sneddon, J. R. Faeder, T. Emonet, Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat. Methods 8, 177-183 (2011).
97. N. M. Borisov, N. I. Markevich, J. B. Hoek, B. N. Kholodenko, Trading the micro-world of combinatorial complexity for the macro-world of protein interaction domains. Biosystems 83, 152-166 (2006).
98. H. Conzelmann, J. Saez-Rodriguez, T. Sauter, B. N. Kholodenko, E. D. Gilles, A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks. BMC Bioinformatics 7, 34 (2006).
99. J. A. Papin, T. Hunter, B. O. Palsson, S. Subramaniam, Reconstruction of cellular signalling networks and analysis of their properties. Nat. Rev. Mol. Cell Biol. 6, 99-111 (2005).
100. D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano, G. Stolovitzky, Revealing strengths and weaknesses of methods for gene network inference. Proc. Natl. Acad. Sci. U.S.A. 107, 6286-6291 (2010).
101. T. Santra, B. Kholodenko, W. Kolch, An integrated Bayesian framework for identifying phosphorylation networks in stimulated cells. Adv. Exp. Med. Biol. 736, 59-80 (2012).
102. B. N. Kholodenko, A. Kiyatkin, F. J. Bruggeman, E. Sontag, H. V. Westerhoff, J. B. Hoek, Untangling the wires: A strategy to trace functional interactions in signaling and gene networks. Proc. Natl. Acad. Sci. U.S.A. 99, 12841-12846 (2002).
103. B. N. Kholodenko, J. B. Hoek, H. V. Westerhoff, G. C. Brown, Quantification of information transfer via cellular signal transduction pathways. FEBS Lett. 414, 430-434 (1997) [published erratum appears in FEBS Lett. 414, 150 (1997)].
104. J. Stark, R. Callard, M. Hubank, From the top down: Towards a predictive biology of signalling networks. Trends Biotechnol. 21, 290-293 (2003).
105. A. de la Fuente, D. P. Makhecha, Unravelling gene networks from noisy under-determined experimental perturbation data. Syst. Biol. (Stevenage) 153, 257-262 (2006).
106. K. H. Cho, S. M. Choo, P. Wellstead, O. Wolkenhauer, A unified framework for unraveling the functional interaction structure of a biomolecular network based on stimulus-response experimental data. FEBS Lett. 579, 4520-4528 (2005).
107. S. D. Santos, P. J. Verveer, P. I. Bastiaens, Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate. Nat. Cell Biol. 9, 324-330 (2007).
108. Y. Shimoni, M. Y. Fink, S. G. Choi, S. C. Sealfon, Plato's cave algorithm: Inferring functional signaling networks from early gene expression shadows. PLOS Comput. Biol. 6, e1000828 (2010).
109. P. Schulthess, N. Blüthgen, From reaction networks to information flow-Using modular response analysis to track information in signaling networks. Methods Enzymol. 500, 397-409 (2011).
110. R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, U. Alon, Network motifs: Simple building blocks of complex networks. Science 298, 824-827 (2002).
111. B. T. Seet, I. Dikic, M. M. Zhou, T. Pawson, Reading protein modifications with interaction domains. Nat. Rev. Mol. Cell Biol. 7, 473-483 (2006).
112. A. Lipshtat, S. P. Purushothaman, R. Iyengar, A. Ma'ayan, Functions of bifans in context of multiple regulatory motifs in signaling networks. Biophys. J. 94, 2566-2579 (2008).
113. S. Mangan, U. Alon, Structure and function of the feed-forward loop network motif. Proc. Natl. Acad. Sci. U.S.A. 100, 11980-11985 (2003).
114. S. Kaplan, A. Bren, E. Dekel, U. Alon, The incoherent feed-forward loop can generate non-monotonic input functions for genes. Mol. Syst. Biol. 4, 203 (2008).
115. L. Goentoro, O. Shoval, M. W. Kirschner, U. Alon, The incoherent feedforward loop can provide fold-change detection in gene regulation. Mol. Cell 36, 894-899 (2009).
116. W. Ma, A. Trusina, H. El-Samad, W. A. Lim, C. Tang, Defining network topologies that can achieve biochemical adaptation. Cell 138, 760-773 (2009).
117. A. Kiyatkin, E. Aksamitiene, N. I. Markevich, N. M. Borisov, J. B. Hoek, B. N. Kholodenko, Scaffolding protein Grb2-associated binder 1 sustains epidermal growth factor-induced mitogenic and survival signaling by multiple positive feedback loops. J. Biol. Chem. 281, 19925-19938 (2006).
118. M. A. Lemmon, J. Schlessinger, Cell signaling by receptor tyrosine kinases. Cell 141, 1117-1134 (2010).
119. N. Borisov, E. Aksamitiene, A. Kiyatkin, S. Legewie, J. Berkhout, T. Maiwald, N. P. Kaimachnikov, J. Timmer, J. B. Hoek, B. N. Kholodenko, Systems-level interactions between insulin-EGF networks amplify mitogenic signaling. Mol. Syst. Biol. 5, 256 (2009).
120. L. O. Murphy, S. Smith, R. H. Chen, D. C. Fingar, J. Blenis, Molecular interpretation of ERK signal duration by immediate early gene products. Nat. Cell Biol. 4, 556-564 (2002).
121. A. Bertotti, P. M. Comoglio, L. Trusolino, Beta4 integrin activates a Shp2-Src signaling pathway that sustains HGF-induced anchorage-independent growth. J. Cell Biol. 175, 993-1003 (2006).
122. A. Goldbeter, D. E. Koshland Jr., An amplified sensitivity arising from covalent modification in biological systems. Proc. Natl. Acad. Sci. U.S.A. 78, 6840-6844 (1981).
123. N. I. Markevich, J. B. Hoek, B. N. Kholodenko, Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. J. Cell Biol. 164, 353-359 (2004).
124. P. Smolen, D. A. Baxter, J. H. Byrne, Bistable MAP kinase activity: A plausible mechanism contributing to maintenance of late long-term potentiation. Am. J. Physiol. Cell Physiol. 294, C503-C515 (2008).
125. J. R. Pomerening, E. D. Sontag, J. E. Ferrell Jr., Building a cell cycle oscillator: Hysteresis and bistability in the activation of Cdc2. Nat. Cell Biol. 5, 346-351 (2003).
126. W. Sha, J. Moore, K. Chen, A. D. Lassaletta, C. S. Yi, J. J. Tyson, J. C. Sible, Hysteresis drives cell-cycle transitions in Xenopus laevis egg extracts. Proc. Natl. Acad. Sci. U.S.A. 100, 975-980 (2003).
127. B. N. Kholodenko, Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol. 7, 165-176 (2006).
128. H. M. Sauro, B. N. Kholodenko, Quantitative analysis of signaling networks. Prog. Biophys. Mol. Biol. 86, 5-43 (2004).
129. D. Matallanas, M. Birtwistle, D. Romano, A. Zebisch, J. Rauch, A. von Kriegsheim, W. Kolch, Raf family kinases: Old dogs have learned new tricks. Genes Cancer 2, 232-260 (2011).
130. O. E. Sturm, R. Orton, J. Grindlay, M. Birtwistle, V. Vyshemirsky, D. Gilbert, M. Calder, A. Pitt, B. Kholodenko, W. Kolch, The mammalian MAPK/ ERK pathway exhibits properties of a negative feedback amplifier. Sci. Signal. 3, ra90 (2010).
131. B. N. Kholodenko, Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur. J. Biochem. 267, 1583-1588 (2000).
132. S. Y. Shin, O. Rath, S. M. Choo, F. Fee, B. McFerran, W. Kolch, K. H. Cho, Positive- and negative-feedback regulations coordinate the dynamic behavior of the Ras-Raf-MEK-ERK signal transduction pathway. J. Cell Sci. 122, 425-435 (2009).
133. H. Shankaran, D. L. Ippolito, W. B. Chrisler, H. Resat, N. Bollinger, L. K. Opresko, H. S. Wiley, Rapid and sustained nuclear-cytoplasmic ERK oscillations induced by epidermal growth factor. Mol. Syst. Biol. 5, 332 (2009).
134. L. K. Nguyen, J. Muñoz-García, H. Maccario, A. Ciechanover, W. Kolch, B. N. Kholodenko, Switches, excitable responses and oscillations in the Ring1B/Bmi1 ubiquitination system. PLoS Comput. Biol. 7, e1002317 (2011).
135. M. A. Tsyganov, W. Kolch, B. N. Kholodenko, The topology design principles that determine the spatiotemporal dynamics of G-protein cascades. Mol. Biosyst. 8, 730-743 (2012).
136. N. I. Markevich, M. A. Tsyganov, J. B. Hoek, B. N. Kholodenko, Long-range signaling by phosphoprotein waves arising from bistability in protein kinase cascades. Mol. Syst. Biol. 2, 61 (2006).
137. B. N. Kholodenko, J. F. Hancock, W. Kolch, Signalling ballet in space and time. Nat. Rev. Mol. Cell Biol. 11, 414-426 (2010).
138. R. J. Prill, J. Saez-Rodriguez, L. G. Alexopoulos, P. K. Sorger, G. Stolovitzky, Crowdsourcing network inference: the DREAM predictive signaling network challenge. Sci. Signal. 4, mr7 (2011).
139. J. Quackenbush, Computational approaches to analysis of DNA microarray data. Yearb. Med. Inform. 2006, 91-103 (2006).
140. J. Cox, M. Mann, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367-1372 (2008).
141. P. Dalle Pezze, A. G. Sonntag, A. Thien, M. T. Prentzell, M. Gödel, S. Fischer, E. Neumann-Haefelin, T. B. Huber, R. Baumeister, D. P. Shanley, K. Thedieck, A dynamic network model of mTOR signaling reveals TSC-independent mTORC2 regulation. Sci. Signal. 5, ra25 (2012).
142. T. R. Xu, V. Vyshemirsky, A. Gormand, A. von Kriegsheim, M. Girolami, G. S. Baillie, D. Ketley, A. J. Dunlop, G. Milligan, M. D. Houslay, W. Kolch, Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species. Sci. Signal. 3, ra20 (2010).
143. H. Yu, P. Braun, M. A. Yildirim, I. Lemmens, K. Venkatesan, J. Sahalie, T. Hirozane-Kishikawa, F. Gebreab, N. Li, N. Simonis, T. Hao, J. F. Rual, A. Dricot, A. Vazquez, R. R. Murray, C. Simon, L. Tardivo, S. Tam, N. Svrzikapa, C. Fan, A. S. de Smet, A. Motyl, M. E. Hudson, J. Park, X. Xin, M. E. Cusick, T. Moore, C. Boone, M. Snyder, F. P. Roth, A. L. Barabási, J. Tavernier, D. E. Hill, M. Vidal, High-quality binary protein interaction map of the yeast interactome network. Science 322, 104-110 (2008).
144. P. Picotti, O. Rinner, R. Stallmach, F. Dautel, T. Farrah, B. Domon, H. Wenschuh, R. Aebersold, High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 7, 43-46 (2010).
145. R. Linding, L. J. Jensen, G. J. Ostheimer, M. A. van Vugt, C. Jørgensen, I. M. Miron, F. Diella, K. Colwill, L. Taylor, K. Elder, P. Metalnikov, V. Nguyen, A. Pasculescu, J. Jin, J. G. Park, L. D. Samson, J. R. Woodgett, R. B. Russell, P. Bork, M. B. Yaffe, T. Pawson, Systematic discovery of in vivo phosphorylation networks. Cell 129, 1415-1426 (2007).
146. B. H. ter Kuile, H. V. Westerhoff, Transcriptome meets metabolome: Hierarchical and metabolic regulation of the glycolytic pathway. FEBS Lett. 500, 169-171 (2001).
147. I. Lee, B. Lehner, C. Crombie, W. Wong, A. G. Fraser, E. M. Marcotte, A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans. Nat. Genet. 40, 181-188 (2008).
148. M. Costanzo, A. Baryshnikova, C. L. Myers, B. Andrews, C. Boone, Charting the genetic interaction map of a cell. Curr. Opin. Biotechnol. 22, 66-74 (2011).
149. C. P. Ponting, P. L. Oliver, W. Reik, Evolution and functions of long noncoding RNAs. Cell 136, 629-641 (2009).
150. A. C. Tuck, D. Tollervey, RNA in pieces. Trends Genet. 27, 422-432 (2011).
151. S. Rogers, M. Girolami, W. Kolch, K. M. Waters, T. Liu, B. Thrall, H. S. Wiley, Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models. Bioinformatics 24, 2894-2900 (2008).
152. J. G. Reich, E. E. Sel'kov, Energy Metabolism of the Cell: A Theoretical Treatise (Academic Press, New York, 1981).
153. H. F. Deus, R. Stanislaus, D. F. Veiga, C. Behrens, I. I. Wistuba, II, J. D. Minna, H. R. Garner, S. G. Swisher, J. A. Roth, A. M. Correa, B. Broom, K. Coombes, A. Chang, L. H. Vogel, J. S. Almeida, A Semantic Web management model for integrative biomedical informatics. PLoS ONE 3, e2946 (2008).
154. E. Antezana, M. Kuiper, V. Mironov, Biological knowledge management: the emerging role of the Semantic Web technologies. Brief. Bioinform. 10, 392-407 (2009).
155. A. L. Barabási, Scale-free networks: A decade and beyond. Science 325, 412-413 (2009).
156. Y. Y. Liu, J. J. Slotine, A. L. Barabási, Controllability of complex networks. Nature 473, 167-173 (2011).
157. F. J. Müller, A. Schuppert, Few inputs can reprogram biological networks. Nature 478, E4; discussion, E4-E5 (2011).
158. A. Ravni, L. E. Eiden, H. Vaudry, B. J. Gonzalez, D. Vaudry, Cycloheximide treatment to identify components of the transitional transcriptome in PACAP-induced PC12 cell differentiation. J. Neurochem. 98, 1229-1241 (2006).
159. Y. Landry, J. P. Gies, Drugs and their molecular targets: an updated overview. Fundam. Clin. Pharmacol. 22, 1-18 (2008).
160. A. Marin-Sanguino, S. K. Gupta, E. O. Voit, J. Vera, Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases. Methods Enzymol. 487, 319-369 (2011).
161. E. E. Schadt, S. H. Friend, D. A. Shaywitz, A network view of disease and compound screening. Nat. Rev. Drug Discov. 8, 286-295 (2009).
162. O. Folger, L. Jerby, C. Frezza, E. Gottlieb, E. Ruppin, T. Shlomi, Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol. 7, 501 (2011).
163. R. L. Chang, L. Xie, L. Xie, P. E. Bourne, B. Ø. Palsson, Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput. Biol. 6, e1000938 (2010).
164. B. Schoeberl, E. A. Pace, J. B. Fitzgerald, B. D. Harms, L. Xu, L. Nie, B. Linggi, A. Kalra, V. Paragas, R. Bukhalid, V. Grantcharova, N. Kohli, K. A. West, M. Leszczyniecka, M. J. Feldhaus, A. J. Kudla, U. B. Nielsen, Therapeutically targeting ErbB3: A key node in ligand-induced activation of the ErbB re-ceptor- PI3K axis. Sci. Signal. 2, ra31 (2009).
165. L. B. Kleiman, T. Maiwald, H. Conzelmann, D. A. Lauffenburger, P. K. Sorger, Rapid phospho-turnover by receptor tyrosine kinases impacts downstream signaling and drug binding. Mol. Cell 43, 723-737 (2011).
166. I. Astsaturov, V. Ratushny, A. Sukhanova, M. B. Einarson, T. Bagnyukova, Y. Zhou, K. Devarajan, J. S. Silverman, N. Tikhmyanova, N. Skobeleva, A. Pecherskaya, R. E. Nasto, C. Sharma, S. A. Jablonski, I. G. Serebriiskii, L. M. Weiner, E. A. Golemis, Synthetic lethal screen of an EGFR-centered network to improve targeted therapies. Sci. Signal. 3, ra67 (2010).
167. P. I. Poulikakos, D. B. Solit, Resistance to MEK inhibitors: Should we co-target upstream? Sci. Signal. 4, pe16 (2011).
168. N. Galanina, V. Bossuyt, L. N. Harris, Molecular predictors of response to therapy for breast cancer. Cancer J. 17, 96-103 (2011).
169. T. Pawson, R. Linding, Network medicine. FEBS Lett. 582, 1266-1270 (2008).
170. A. L. Barabási, N. Gulbahce, J. Loscalzo, Network medicine: A network-based approach to human disease. Nat. Rev. Genet. 12, 56-68 (2011).
171. K. I. Goh, M. E. Cusick, D. Valle, B. Childs, M. Vidal, A. L. Barabási, The human disease network. Proc. Natl. Acad. Sci. U.S.A. 104, 8685-8690 (2007).
172. I. W. Taylor, R. Linding, D. Warde-Farley, Y. Liu, C. Pesquita, D. Faria, S. Bull, T. Pawson, Q. Morris, J. L. Wrana, Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. 27, 199-204 (2009).