Mass spectrometry-based proteomics and network biology

Annual Review of Biochemistry

Volumn 81, Issue , 2012, Pages 379-405

  Bensimon, Ariel ^a,b   Heck, Albert J R ^a,c   Aebersold, Ruedi ^a,b,d  

^a Institute of Molecular Systems Biology   (Switzerland)

^b ETH ZURICH   (Switzerland)

^c UTRECHT UNIVERSITY   (Netherlands)

^d UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

data driven modeling; posttranslational modification; protein interaction network; protein signaling network; quantitative proteomics; systems biology

Indexed keywords

PROTEOME;

ARTICLE; GENOTYPE; HUMAN; MASS SPECTROMETRY; MEDICAL TECHNOLOGY; NONHUMAN; PHENOTYPE; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEOMICS; QUANTITATIVE ANALYSIS; SYSTEMS BIOLOGY;

ANIMALS; HUMANS; MASS SPECTROMETRY; PROTEIN INTERACTION MAPS; PROTEOMICS; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

EID: 84861897793     PISSN: 00664154     EISSN: 15454509     Source Type: Book Series    
DOI: 10.1146/annurev-biochem-072909-100424     Document Type: Article

References (153)

1. Beadle GW, Tatum EL. 1941. Genetic control of biochemical reactions in Neurospora. Proc. Natl. Acad. Sci. USA 27:499-506
2. Hawkins RD, Hon GC, Ren B. 2010. Next-generation genomics: an integrative approach. Nat. Rev. Genet. 11:476-86
3. Zhou X, Ren L, Meng Q, Li Y, Yu Y, Yu J. 2010. The next-generation sequencing technology and application. Protein Cell 1:520-36
4. Cox J, Mann M. 2011. Quantitative, high-resolution proteomics for data-driven systems biology. Annu. Rev. Biochem. 80:273-99
5. Le Provost F, Lillico S, Passet B, YoungR, Whitelaw B, Vilotte JL. 2010. Zinc finger nuclease technology heralds a new era in mammalian transgenesis. Trends Biotechnol. 28:134-41
6. Boutros M, Ahringer J. 2008. The art and design of genetic screens: RNA interference. Nat. Rev. Genet. 9:554-66
7. Zhang EE, Liu AC, Hirota T, Miraglia LJ, Welch G, et al. 2009. A genome-wide RNAi screen for modifiers of the circadian clock in human cells. Cell 139:199-210
8. Kondo S, PerrimonN. 2011. A genome-wide RNAi screen identifies core components of theG-MDNA damage checkpoint. Sci. Signal. 4:rs1
9. Shapira SD, Gat-Viks I, Shum BO, Dricot A, de Grace MM, et al. 2009. A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection. Cell 139:1255-67
10. Cazier JB, Tomlinson I. 2010. General lessons from large-scale studies to identify human cancer predisposition genes. J. Pathol. 220:255-62
11. Amberger J, Bocchini C, Hamosh A. 2011. A new face and new challenges for OnlineMendelian Inheritance in Man (OMIMR-). Hum. Mutat. 32:564-67
12. Arkin AP, Schaffer DV. 2011. Network news: innovations in 21st century systems biology. Cell 144:844-49
13. Kreeger PK, Lauffenburger DA. 2010. Cancer systems biology: a network modeling perspective. Carcinogenesis 31:2-8
14. Pe'er D, Hacohen N. 2011. Principles and strategies for developing network models in cancer. Cell 144:864-73
15. Vidal M, Cusick ME, Barabasi AL. 2011. Interactome networks and human disease. Cell 144:986-98
16. Yates JR, Ruse CI, Nakorchevsky A. 2009. Proteomics by mass spectrometry: approaches, advances, and applications. Annu. Rev. Biomed. Eng. 11:49-79
17. Domon B, Aebersold R. 2010. Options and considerations when selecting a quantitative proteomics strategy. Nat. Biotechnol. 28:710-21
18. Nesvizhskii AI, Vitek O, Aebersold R. 2007. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4:787-97
19. Mallick P, Kuster B. 2010. Proteomics: a pragmatic perspective. Nat. Biotechnol. 28:695-709
20. Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, et al. 2002. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1:376-86
21. Mueller LN, Rinner O, Schmidt A, Letarte S, Bodenmiller B, et al. 2007. SuperHirn-a novel tool for high resolution LC-MS-based peptide/protein profiling. Proteomics 7:3470-80
22. Neilson KA, Ali NA, Muralidharan S, Mirzaei M, Mariani M, et al. 2011. Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 11:535-53
23. Jaffe JD, Keshishian H, Chang B, Addona TA, Gillette MA, Carr SA. 2008. Accurate inclusion mass screening: a bridge from unbiased discovery to targeted assay development for biomarker verification. Mol. Cell. Proteomics 7:1952-62
24. Schmidt A, Claassen M, Aebersold R. 2009. Directed mass spectrometry: towards hypothesis-driven proteomics. Curr. Opin. Chem. Biol. 13:510-17
25. Lange V, Picotti P, Domon B, Aebersold R. 2008. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 4:222
26. Gallien S, Duriez E, Domon B. 2011. Selected reaction monitoring applied to proteomics. J. Mass Spectrom. 46:298-312
27. Picotti P, Rinner O, Stallmach R, Dautel F, Farrah T, et al. 2010. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 7:43-46
28. Addona TA, Abbatiello SE, Schilling B, Skates SJ, Mani DR, et al. 2009. Multi-site assessment of the precision and reproducibility ofmultiple reactionmonitoring-basedmeasurements of proteins in plasma. Nat. Biotechnol. 27:633-41
29. Geromanos SJ, Vissers JP, Silva JC, Dorschel CA, Li GZ, et al. 2009. The detection, correlation, and comparison of peptide precursor and product ions from data independent LC-MS with data dependant LC-MS/MS. Proteomics 9:1683-95
30. Geiger T, Cox J, Mann M. 2010. Proteomics on an Orbitrap benchtop mass spectrometer using all-ion fragmentation. Mol. Cell. Proteomics 9:2252-61
31. Venable JD, Dong MQ, Wohlschlegel J, Dillin A, Yates JR. 2004. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1:39-45
32. Panchaud A, Scherl A, Shaffer SA, von Haller PD, Kulasekara HD, et al. 2009. Precursor acquisition independent from ion count: how to dive deeper into the proteomics ocean. Anal. Chem. 81:6481-88
33. Gillet LC, Navarro P, Tate S, R̈ ost H, Selevsek N, et al. 2012. Targeted data extraction of the MS/MS spectra generated by data independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics. In press
34. Ahrens CH, Brunner E, Qeli E, Basler K, Aebersold R. 2010. Generating and navigating proteomemaps using mass spectrometry. Nat. Rev. Mol. Cell Biol. 11:789-801
35. de Godoy LM, Olsen JV, Cox J, Nielsen ML, Hubner NC, et al. 2008. Comprehensive massspectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455:1251-54
36. Malmstrom J, Beck M, Schmidt A, Lange V, Deutsch EW, Aebersold R. 2009. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460:762-65
37. Brunner E, Ahrens CH, Mohanty S, Baetschmann H, Loevenich S, et al. 2007. A high-quality catalog of the Drosophila melanogaster proteome. Nat. Biotechnol. 25:576-83
38. Munoz J, Low TY, Kok YJ, Chin A, Frese CK, et al. 2011. The quantitative proteomes of human-induced pluripotent stem cells and embryonic stem cells. Mol. Syst. Biol. 7:550
39. Nagaraj N, Wisniewski JR, Geiger T, Cox J, Kircher M, et al. 2011. Deep proteome and transcriptome mapping of a human cancer cell line. Mol. Syst. Biol. 7:548
40. Beck M, Schmidt A, Malmstroem J, Claassen M, Ori A, et al. 2011. The quantitative proteome of a human cell line. Mol. Syst. Biol. 7:549
41. ChoudharyC, Kumar C, Gnad F, NielsenML, Rehman M, et al. 2009. Lysine acetylation targets protein complexes and co-regulates major cellular functions. Science 325:834-40
42. Huttlin EL, Jedrychowski MP, Elias JE, Goswami T, Rad R, et al. 2010. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143:1174-89
43. ZielinskaDF, Gnad F, Wisniewski JR, MannM. 2010. Precisionmapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints. Cell 141:897-907
44. Picotti P, Bodenmiller B, Mueller LN, Domon B, Aebersold R. 2009. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138:795-806
45. Costenoble R, Picotti P, Reiter L, Stallmach R, HeinemannM, et al. 2011. Comprehensive quantitative analysis of central carbon and amino-acid metabolism in Saccharomyces cerevisiae undermultiple conditions by targeted proteomics. Mol. Syst. Biol. 7:464
46. Olsen JV, Vermeulen M, Santamaria A, Kumar C, Miller ML, et al. 2010. Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal. 3:ra3
47. Bodenmiller B, Wanka S, Kraft C, Urban J, Campbell D, et al. 2010. Phosphoproteomic analysis reveals interconnected system-wide responses to perturbations of kinases and phosphatases in yeast. Sci. Signal. 3:rs4
48. Van Hoof D, Mũ noz J, Braam SR, PinkseMW, Linding R, et al. 2009. Phosphorylation dynamics during early differentiation of human embryonic stem cells. Cell Stem Cell 5:214-26
49. Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, et al. 2011. Global quantification of mammalian gene expression control. Nature 473:337-42
50. Schmidt A, BeckM, Malmstr ̈om J, Lam H, ClaassenM, et al. 2011. Absolute quantification of microbial proteomes at different states by directed mass spectrometry. Mol. Syst. Biol. 7:510
51. Huang da W, Sherman BT, Lempicki RA. 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4:44-57
52. Ma'ayan A. 2008. Network integration and graph analysis in mammalian molecular systems biology. IET Syst. Biol. 2:206-21
53. GehlenborgN, O'Donoghue SI, BaligaNS, Goesmann A, Hibbs MA, et al. 2010. Visualization of omics data for systems biology. Nat. Methods 7:S56-68
54. Park PJ. 2009. ChIP-seq: advantages and challenges of amaturing technology. Nat. Rev. Genet. 10:669-80
55. Suter B, Kittanakom S, Stagljar I. 2008. Two-hybrid technologies in proteomics research. Curr. Opin. Biotechnol. 19:316-23
56. Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, et al. 2001. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292:929-34
57. Yu H, Braun P, Yildirim MA, Lemmens I, Venkatesan K, et al. 2008. High-quality binary protein interaction map of the yeast interactome network. Science 322:104-10
58. Bandyopadhyay S, Mehta M, Kuo D, SungMK, Chuang R, et al. 2010. Rewiring of genetic networks in response to DNA damage. Science 330:1385-89
59. Butter F, Scheibe M, Morl M, Mann M. 2009. Unbiased RNA-protein interaction screen by quantitative proteomics. Proc. Natl. Acad. Sci. USA 106:10626-31
60. Bantscheff M, Scholten A, Heck AJ. 2009. Revealing promiscuous drug-target interactions by chemical proteomics. Drug Discov. Today 14:1021-29
61. Rix U, Superti-Furga G. 2009. Target profiling of small molecules by chemical proteomics. Nat. Chem. Biol. 5:616-24
62. Li X, Gianoulis TA, Yip KY, Gerstein M, Snyder M. 2010. Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses. Cell 143:639-50
63. Gingras AC, Gstaiger M, Raught B, Aebersold R. 2007. Analysis of protein complexes using mass spectrometry. Nat. Rev. Mol. Cell Biol. 8:645-54
64. Sharon M, Robinson CV. 2007. The role of mass spectrometry in structure elucidation of dynamic protein complexes. Annu. Rev. Biochem. 76:167-93
65. Heck AJ. 2008. Native mass spectrometry: a bridge between interactomics and structural biology. Nat. Methods 5:927-33
66. LeitnerA, WalzthoeniT, KahramanA, Herzog F, RinnerO, et al. 2010. Probing native protein structures by chemical cross-linking, mass spectrometry, and bioinformatics. Mol. Cell. Proteomics 9:1634-49
67. Sigal A, Danon T, Cohen A, Milo R, Geva-ZatorskyN, et al. 2007. Generation of a fluorescently labeled endogenous protein library in living human cells. Nat. Protoc. 2:1515-27
68. de Boer E, Rodriguez P, Bonte E, Krijgsveld J, Katsantoni E, et al. 2003. Efficient biotinylation and single-step purification of tagged transcription factors in mammalian cells and transgenic mice. Proc. Natl. Acad. Sci. USA 100:7480-85
69. Hubner NC, Bird AW, Cox J, Splettstoesser B, Bandilla P, et al. 2010. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 189:739-54
70. Malovannaya A, Lanz RB, Jung SY, Bulynko Y, Le NT, et al. 2011. Analysis of the human endogenous coregulator complexome. Cell 145:787-99
71. Boulon S, Ahmad Y, Trinkle-Mulcahy L, Verheggen C, Cobley A, et al. 2010. Establishment of a protein frequency library and its application in the reliable identification of specific protein interaction partners. Mol. Cell. Proteomics 9:861-79
72. Trinkle-Mulcahy L, Boulon S, Lam YW, Urcia R, Boisvert FM, et al. 2008. Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes. J. Cell Biol. 183:223-39
73. RinnerO, Mueller LN, Hub́alekM, M̈ ullerM, Gstaiger M, Aebersold R. 2007. An integrated mass spectrometric and computational framework for the analysis of protein interaction networks. Nat. Biotechnol. 25:345-52
74. Glatter T, Wepf A, Aebersold R, Gstaiger M. 2009. An integrated workflow for charting the human interaction proteome: insights into the PP2A system. Mol. Syst. Biol. 5:237
75. Sardiu ME, Cai Y, Jin J, Swanson SK, Conaway RC, et al. 2008. Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics. Proc. Natl. Acad. Sci. USA 105:1454-59
76. Sowa ME, Bennett EJ, Gygi SP, Harper JW. 2009. Defining the human deubiquitinating enzyme interaction landscape. Cell 138:389-403
77. Choi H, Larsen B, Lin ZY, Breitkreutz A, Mellacheruvu D, et al. 2011. SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat. Methods 8:70-73
78. Behrends C, Sowa ME, Gygi SP, Harper JW. 2010. Network organization of the human autophagy system. Nature 466:68-76
79. Kuhner S, Van Noort V, Betts MJ, Leo-Macias A, Batisse C, et al. 2009. Proteome organization in a genome-reduced bacterium. Science 326:1235-40
80. Breitkreutz A, Choi H, Sharom JR, Boucher L, Neduva V, et al. 2010. A global protein kinase and phosphatase interaction network in yeast. Science 328:1043-46
81. Vermeulen M, Eberl HC, Matarese F, Marks H, Denissov S, et al. 2010. Quantitative interaction proteomics and genome-wide profiling of epigenetic histone marks and their readers. Cell 142:967-80
82. Lee KK, Sardiu ME, Swanson SK, Gilmore JM, TorokM, et al. 2011. Combinatorial depletion analysis to assemble the network architecture of the SAGA and ADA chromatin remodeling complexes. Mol. Syst. Biol. 7:503
83. Wang J, Li M, Deng Y, Pan Y. 2010. Recent advances in clustering methods for protein interaction networks. BMC Genomics 11(Suppl. 3):S10
84. Choi H, Kim S, Gingras AC, Nesvizhskii AI. 2010. Analysis of protein complexes through model-based biclustering of label-free quantitative AP-MS data. Mol. Syst. Biol. 6:385
85. Scott JD, Pawson T. 2009. Cell signaling in space and time: where proteins come together and when they're apart. Science 326:1220-24
86. BodenmillerB, MuellerLN, MuellerM, Domon B, AebersoldR. 2007. Reproducible isolation of distinct, overlapping segments of the phosphoproteome. Nat. Methods 4:231-37
87. Lemeer S, Heck AJ. 2009. The phosphoproteomics data explosion. Curr. Opin. Chem. Biol. 13:414-20
88. Thingholm TE, Jensen ON, Larsen MR. 2009. Analytical strategies for phosphoproteomics. Proteomics 9:1451-68
89. Hornbeck PV, Chabra I, Kornhauser JM, Skrzypek E, Zhang B. 2004. PhosphoSite: a bioinformatics resource dedicated to physiological protein phosphorylation. Proteomics 4:1551-61
90. Cutillas PR, Jørgensen C. 2011. Biological signalling activity measurements using mass spectrometry. Biochem. J. 434:189-99
91. Tan CS, Linding R. 2009. Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks. Proteomics 9:5233-42
92. Mok J, Im H, Snyder M. 2009. Global identification of protein kinase substrates by protein microarray analysis. Nat. Protoc. 4:1820-27
93. Mok J, Kim PM, Lam HY, Piccirillo S, Zhou X, et al. 2010. Deciphering protein kinase specificity through large-scale analysis of yeast phosphorylation site motifs. Sci. Signal. 3:ra12
94. Allen JJ, Li M, Brinkworth CS, Paulson JL, Wang D, et al. 2007. A semisynthetic epitope for kinase substrates. Nat. Methods 4:511-16
95. Blethrow JD, Glavy JS, Morgan DO, Shokat KM. 2008. Covalent capture of kinase-specific phosphopeptides reveals Cdk1-cyclin B substrates. Proc. Natl. Acad. Sci. USA 105:1442-47
96. Bloom J, Cristea IM, Procko AL, Lubkov V, Chait BT, et al. 2011. Global analysis of Cdc14 phosphatase reveals diverse roles in mitotic processes. J. Biol. Chem. 286:5434-45
97. Maly DJ, Allen JA, Shokat KM. 2004. A mechanism-based cross-linker for the identification of kinasesubstrate pairs. J. Am. Chem. Soc. 126:9160-61
98. Linding R, Jensen LJ, Pasculescu A, Olhovsky M, Colwill K, et al. 2008. NetworKIN: a resource for exploring cellular phosphorylation networks. Nucleic Acids Res. 36:D695-99
99. Karlebach G, Shamir R. 2008. Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9:770-80
100. De Smet R, Marchal K. 2010. Advantages and limitations of current network inference methods. Nat. Rev. Microbiol. 8:717-29
101. Helbig AO, Gauci S, Raijmakers R, Van Breukelen B, SlijperM, et al. 2010. Profiling of N-acetylated protein termini provides in-depth insights into the N-terminal nature of the proteome. Mol. Cell. Proteomics 9:928-39
102. Kerscher O, Felberbaum R, HochstrasserM. 2006. Modification of proteins by ubiquitin and ubiquitinlike proteins. Annu. Rev. Cell Dev. Biol. 22:159-80
103. Shi Y, Xu P, Qin J. 2011. Ubiquitinated proteome: ready for global? Mol. Cell. Proteomics 10:R110.006882
104. Argenzio E, Bange T, Oldrini B, Bianchi F, Peesari R, et al. 2011. Proteomic snapshot of the EGFinduced ubiquitin network. Mol. Syst. Biol. 7:462
105. KimW, Bennett EJ, Huttlin EL, Guo A, Li J, et al. 2011. Systematic and quantitative assessment of the ubiquitin-modified proteome. Mol. Cell 44:325-40
106. Wagner SA, Beli P, Weinert BT, Nielsen ML, Cox J, et al. 2011. A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles. Mol. Cell. Proteomics 10:M111 013284
107. Schilling O, Keller U, Overall CM. 2011. Protease specificity profiling by tandem mass spectrometry using proteome-derived peptide libraries. Methods Mol. Biol. 753:257-72
108. Nomura DK, Dix MM, Cravatt BF. 2010. Activity-based protein profiling for biochemical pathway discovery in cancer. Nat. Rev. Cancer 10:630-38
109. Mahrus S, Trinidad JC, Barkan DT, Sali A, Burlingame AL, Wells JA. 2008. Global sequencing of proteolytic cleavage sites in apoptosis by specific labeling of protein N termini. Cell 134:866-76
110. Kleifeld O, Doucet A, Keller U, Prudova A, SchillingO, et al. 2010. Isotopic labeling of terminal amines in complex samples identifies protein N-termini and protease cleavage products. Nat. Biotechnol. 28:281-88
111. von Kriegsheim A, Baiocchi D, Birtwistle M, Sumpton D, Bienvenut W, et al. 2009. Cell fate decisions are specified by the dynamic ERK interactome. Nat. Cell Biol. 11:1458-64
112. Baker CL, Kettenbach AN, Loros JJ, Gerber SA, Dunlap JC. 2009. Quantitative proteomics reveals a dynamic interactome and phase-specific phosphorylation in the Neurospora circadian clock. Mol. Cell 34:354-63
113. Aye TT, Soni S, Van Veen TA, Van der Heyden MA, Cappadona S, et al. 2012. Reorganized PKA-AKAP associations in the failing human heart. J. Mol. Cell. Cardiol. 52:511-18
114. Wepf A, Glatter T, Schmidt A, Aebersold R, Gstaiger M. 2009. Quantitative interaction proteomics using mass spectrometry. Nat. Methods 6:203-5
115. Bennett EJ, Rush J, Gygi SP, Harper JW. 2010. Dynamics of cullin-RING ubiquitin ligase network revealed by systematic quantitative proteomics. Cell 143:951-65
116. Gerber SA, Rush J, Stemman O, KirschnerMW, Gygi SP. 2003. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. USA 100:6940-45
117. Bisson N, James DA, Ivosev G, Tate SA, Bonner R, et al. 2011. Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor. Nat. Biotechnol. 29:653-58
118. Zhong Q, Simonis N, Li QR, Charloteaux B, Heuze F, et al. 2009. Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol. 5:321
119. Bensimon A, Schmidt A, Ziv Y, Elkon R, Wang SY, et al. 2010. ATM-dependent and-independent dynamics of the nuclear phosphoproteome after DNA damage. Sci. Signal. 3:rs3
120. Yu Y, Yoon SO, Poulogiannis G, Yang Q, Ma XM, et al. 2011. Phosphoproteomic analysis identifies Grb10 as an mTORC1 substrate that negatively regulates insulin signaling. Science 332:1322-26
121. KaramanMW, Herrgard S, Treiber DK, Gallant P, Atteridge CE, et al. 2008. A quantitative analysis of kinase inhibitor selectivity. Nat. Biotechnol. 26:127-32
122. Koch A, Hauf S. 2010. Strategies for the identification of kinase substrates using analog-sensitive kinases. Eur. J. Cell Biol. 89:184-93
123. Holt LJ, Tuch BB, Villen J, Johnson AD, Gygi SP, Morgan DO. 2009. Global analysis of Cdk1 substrate phosphorylation sites provides insights into evolution. Science 325:1682-86
124. Matsuoka S, Ballif BA, Smogorzewska A, McDonald ER 3rd, Hurov KE, et al. 2007. ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage. Science 316:1160-66
125. Moritz A, Li Y, Guo A, Villen J, Wang Y, et al. 2010. Akt-RSK-S6 kinase signaling networks activated by oncogenic receptor tyrosine kinases. Sci. Signal. 3:ra64
126. Mayya V, Lundgren DH, Hwang SI, Rezaul K, Wu L, et al. 2009. Quantitative phosphoproteomic analysis of T cell receptor signaling reveals system-wide modulation of protein-protein interactions. Sci. Signal. 2:ra46
127. Jørgensen C, Sherman A, Chen GI, Pasculescu A, Poliakov A, et al. 2009. Cell-specific information processing in segregating populations of Eph receptor ephrin-expressing cells. Science 326:1502-9
128. Coba MP, Pocklington AJ, Collins MO, Kopanitsa MV, Uren RT, et al. 2009. Neurotransmitters drive combinatorial multistate postsynaptic density networks. Sci. Signal. 2:ra19
129. Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, et al. 2009. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. 27:199-204
130. Mani KM, LefebvreC, WangK, Lim WK, Basso K, et al. 2008. A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol. Syst. Biol. 4:169
131. Chang JT, Carvalho C, Mori S, Bild AH, Gatza ML, et al. 2009. A genomic strategy to elucidatemodules of oncogenic pathway signaling networks. Mol. Cell 34:104-14
132. Nibbe RK, Koyut ̈ urk M, Chance MR. 2010. An integrative-omics approach to identify functional subnetworks in human colorectal cancer. PLoS Comput. Biol. 6:e1000639
133. Lage K, Mollgard K, Greenway S, Wakimoto H, Gorham JM, et al. 2010. Dissecting spatio-temporal protein networks driving human heart development and related disorders. Mol. Syst. Biol. 6:381
134. Rikova K, Guo A, Zeng Q, Possemato A, Yu J, et al. 2007. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131:1190-203
135. Cutillas PR, Khwaja A, Graupera M, Pearce W, Gharbi S, et al. 2006. Ultrasensitive and absolute quantification of the phosphoinositide 3-kinase/Akt signal transduction pathway by mass spectrometry. Proc. Natl. Acad. Sci. USA 103:8959-64
136. Kubota K, Anjum R, Yu Y, Kunz RC, Andersen JN, et al. 2009. Sensitive multiplexed analysis of kinase activities and activity-based kinase identification. Nat. Biotechnol. 27:933-40
137. Daub H, Olsen JV, Bairlein M, Gnad F, Oppermann FS, et al. 2008. Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. Mol. Cell 31:438-48
138. Bantscheff M, Eberhard D, Abraham Y, Bastuck S, Boesche M, et al. 2007. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors. Nat. Biotechnol. 25:1035-44
139. Wodicka LM, Ciceri P, DavisMI, Hunt JP, Floyd M, et al. 2010. Activation state-dependent binding of small molecule kinase inhibitors: structural insights from biochemistry. Chem. Biol. 17:1241-49
140. Janes KA, Yaffe MB. 2006. Data-driven modelling of signal-transduction networks. Nat. Rev. Mol. Cell Biol. 7:820-28
141. Janes KA, Albeck JG, Gaudet S, Sorger PK, Lauffenburger DA, Yaffe MB. 2005. A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310:1646-53
142. Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger DA, et al. 2009. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol. Syst. Biol. 5:331
143. Cosgrove BD, Alexopoulos LG, Hang TC, Hendriks BS, Sorger PK, et al. 2010. Cytokine-associated drug toxicity in human hepatocytes is associated with signaling network dysregulation. Mol. BioSyst. 6:1195-206
144. Wu Y, Johnson GL, Gomez SM. 2008. Data-driven modeling of cellular stimulation, signaling and output response in RAW 264.7 cells. J. Mol. Signal. 3:11
145. Nelander S, WangW, Nilsson B, She QB, Pratilas C, et al. 2008. Models from experiments: combinatorial drug perturbations of cancer cells. Mol. Syst. Biol. 4:216
146. Mitsos A, Melas IN, Siminelakis P, Chairakaki AD, Saez-Rodriguez J, Alexopoulos LG. 2009. Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Comput. Biol. 5:e1000591
147. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, et al. 2010. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26:966-68
148. Brusniak MY, Kwok ST, Christiansen M, Campbell D, Reiter L, et al. 2011. ATAQS: a computational software tool for high throughput transition optimization and validation for selected reactionmonitoring mass spectrometry. BMC Bioinform. 12:78
149. Reiter L, Rinner O, Picotti P, Huttenhain R, Beck M, et al. 2011. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat. Methods 8:430-35
150. White FM. 2011. The potential cost of high-throughput proteomics. Sci. Signal. 4:pe8
151. Reiter L, Claassen M, Schrimpf SP, Jovanovic M, Schmidt A, et al. 2009. Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol. Cell. Proteomics 8:2405-17
152. Kreutz C, Timmer J. 2009. Systems biology: experimental design. FEBS J. 276:923-42
153. Oberg AL, Vitek O. 2009. Statistical design of quantitative mass spectrometry-based proteomic experiments. J. Proteome Res. 8:2144-56