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  • Review Article
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Reconstruction of cellular signalling networks and analysis of their properties

Key Points

  • Cellular signalling networks are beginning to be reconstructed at a genome-scale.

  • An order-of-magnitude analysis of the human signalling network provides an estimate of the number of network components, their degree of interconnectivity, and informative functional constraints on network function.

  • Signalling network reconstructions are expanding in scope and detail through the development of new experimental approaches.

  • With integrative and iterative approaches, network reconstructions can be refined to provide increasingly more accurate representations of signalling systems.

  • Structural analyses of signalling networks have successfully identified crucial network components, and allowed for mathematical definitions of network properties.

  • When parameters are well characterized, dynamic analyses of signalling networks have successfully modelled time profiles of component concentrations, which provides insight into network function.

Abstract

The study of cellular signalling over the past 20 years and the advent of high-throughput technologies are enabling the reconstruction of large-scale signalling networks. After careful reconstruction of signalling networks, their properties must be described within an integrative framework that accounts for the complexity of the cellular signalling network and that is amenable to quantitative modelling.

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Figure 1: Integrative and iterative process of cellular signalling network reconstruction.
Figure 2: Structural analyses of signalling networks.
Figure 3: Dynamic analyses of cellular signalling networks.

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References

  1. Finkel, T. & Gutkind, J. S. Signal transduction and human disease (Wiley–Liss, Hoboken, New Jersey, USA, 2003).

    Google Scholar 

  2. Li, J. et al. The Molecule Pages database. Nature 420, 716–717 (2002).

    CAS  PubMed  Google Scholar 

  3. Levchenko, A. Dynamical and integrative cell signaling: challenges for the new biology. Biotechnol. Bioeng. 84, 773–782 (2003).

    CAS  PubMed  Google Scholar 

  4. Weng, G., Bhalla, U. S. & Iyengar, R. Complexity in biological signaling systems. Science 284, 92–96 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Sivakumaran, S., Hariharaputran, S., Mishra, J. & Bhalla, U. S. The database of quantitative cellular signaling: management and analysis of chemical kinetic models of signaling networks. Bioinformatics 19, 408–415 (2003).

    CAS  PubMed  Google Scholar 

  6. Gilman, A. G. et al. Overview of the Alliance for Cellular Signaling. Nature 420, 703–706 (2002). Provides an overview of the first coordinated, multi-institutional effort to systematically unravel the signalling mechanisms of model systems.

    CAS  PubMed  Google Scholar 

  7. Ge, H., Walhout, A. J. & Vidal, M. Integrating 'omic' information: a bridge between genomics and systems biology. Trends Genet. 19, 551–560 (2003). Emphasizes the need for integrating and reconciling data from several experimental sources to create the most accurate representation of biochemical networks.

    CAS  PubMed  Google Scholar 

  8. Helmke, B. P. & Schwartz, M. A. Putting the squeeze on mechanotransduction. Dev. Cell 6, 745–746 (2004).

    CAS  PubMed  Google Scholar 

  9. Ingber, D. E. Tensegrity I. Cell structure and hierarchical systems biology. J. Cell Sci. 116, 1157–1173 (2003).

    CAS  PubMed  Google Scholar 

  10. Ingber, D. E. Tensegrity II. How structural networks influence cellular information processing networks. J. Cell Sci. 116, 1397–1408 (2003).

    CAS  PubMed  Google Scholar 

  11. Pennisi, E. Human genome. A low number wins the GeneSweep Pool. Science 300, 1484 (2003).

    CAS  PubMed  Google Scholar 

  12. Hood, L. & Galas, D. The digital code of DNA. Nature 421, 444–448 (2003).

    PubMed  Google Scholar 

  13. Vander, A. J., Sherman, J. H. & Luciano, D. S. Human physiology: the mechanisms of body function (WCB McGraw–Hill, Boston, Massachusetts, USA 1998).

    Google Scholar 

  14. Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

    CAS  PubMed  Google Scholar 

  15. Manning, G., Whyte, D. B., Martinez, R., Hunter, T. & Sudarsanam, S. The protein kinase complement of the human genome. Science 298, 1912–1934 (2002). The authors present the first systematic inventory of a complete set of signalling network components (protein kinases) in the human cell.

    CAS  PubMed  Google Scholar 

  16. Wang, W. Q., Sun, J. P. & Zhang, Z. Y. An overview of the protein tyrosine phosphatase superfamily. Curr. Top. Med. Chem. 3, 739–748 (2003).

    CAS  PubMed  Google Scholar 

  17. Forrest, A. R. et al. Phosphoregulators: protein kinases and protein phosphatases of mouse. Genome Res. 13, 1443–1454 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Alonso, A. et al. Protein tyrosine phosphatases in the human genome. Cell 117, 699–711 (2004).

    CAS  PubMed  Google Scholar 

  19. Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001). This, and reference 14, reported the initial sequence of the human genome. They provide a glimpse into the 'parts list' of signalling networks from which further reconstructions and analyses will be developed.

    CAS  PubMed  Google Scholar 

  20. Roberts, G. C. & Smith, C. W. Alternative splicing: combinatorial output from the genome. Curr. Opin. Chem. Biol. 6, 375–383 (2002).

    CAS  PubMed  Google Scholar 

  21. Modrek, B. & Lee, C. A genomic view of alternative splicing. Nature Genet. 30, 13–19 (2002).

    CAS  PubMed  Google Scholar 

  22. Thanaraj, T. A. et al. ASD: the alternative splicing database. Nucleic Acids Res. 32, D64–D69 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Hirano, F. et al. Alternative splicing variants of IκBβ establish differential NF-κB signal responsiveness in human cells. Mol. Cell. Biol. 18, 2596–2607 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Modrek, B., Resch, A., Grasso, C. & Lee, C. Genome-wide detection of alternative splicing in expressed sequences of human genes. Nucleic Acids Res. 29, 2850–2859 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. O'Donovan, C., Apweiler, R. & Bairoch, A. The human proteomics initiative (HPI). Trends Biotechnol. 19, 178–181 (2001).

    CAS  PubMed  Google Scholar 

  26. Heaney, M. L. & Golde, D. W. Soluble receptors in human disease. J. Leukoc. Biol. 64, 135–146 (1998).

    CAS  PubMed  Google Scholar 

  27. Service, R. F. Proteomics. High-speed biologists search for gold in proteins. Science 294, 2074–2077 (2001).

    CAS  PubMed  Google Scholar 

  28. Kiekhaefer, C. M., Grass, J. A., Johnson, K. D., Boyer, M. E. & Bresnick, E. H. Hematopoietic-specific activators establish an overlapping pattern of histone acetylation and methylation within a mammalian chromatin domain. Proc. Natl Acad. Sci. USA 99, 14309–14314 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Fischle, W., Wang, Y. & Allis, C. D. Binary switches and modification cassettes in histone biology and beyond. Nature 425, 475–479 (2003).

    Article  CAS  PubMed  Google Scholar 

  30. Park, S. H., Zarrinpar, A. & Lim, W. A. Rewiring MAP kinase pathways using alternative scaffold assembly mechanisms. Science 299, 1061–1064 (2003).

    CAS  PubMed  Google Scholar 

  31. Grigoriev, A. On the number of protein–protein interactions in the yeast proteome. Nucleic Acids Res. 31, 4157–4161 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Hlavacek, W. S., Faeder, J. R., Blinov, M. L., Perelson, A. S. & Goldstein, B. The complexity of complexes in signal transduction. Biotechnol. Bioeng. 84, 783–794 (2003).

    CAS  PubMed  Google Scholar 

  33. Vassilatis, D. K. et al. The G protein-coupled receptor repertoires of human and mouse. Proc. Natl Acad. Sci. USA 100, 4903–4908 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Papin, J. A., Price, N. D., Wiback, S. J., Fell, D. & Palsson, B. O. Metabolic pathways in the post-genome era. Trends Biochem. Sci. 28, 250–258 (2003).

    CAS  PubMed  Google Scholar 

  35. Bornheimer, S. J., Maurya, M. R., Farquhar, M. G. & Subramaniam, S. Computational modeling reveals how interplay between components of the GTPase-cycle module regulates signal transduction. Proc. Natl Acad. Sci. USA 101, 15899–15904 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Schoeberl, B., Eichler-Jonsson, C., Gilles, E. D. & Muller, G. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nature Biotechnol. 20, 370–375 (2002).

    Google Scholar 

  37. Kholodenko, B. N., Demin, O. V., Moehren, G. & Hoek, J. B. Quantification of short term signaling by the epidermal growth factor receptor. J. Biol. Chem. 274, 30169–30181 (1999).

    CAS  PubMed  Google Scholar 

  38. Masui, H., Castro, L. & Mendelsohn, J. Consumption of EGF by A431 cells: evidence for receptor recycling. J. Cell Biol. 120, 85–93 (1993).

    CAS  PubMed  Google Scholar 

  39. Buss, J. E., Kudlow, J. E., Lazar, C. S. & Gill, G. N. Altered epidermal growth factor (EGF)-stimulated protein kinase activity in variant A431 cells with altered growth responses to EGF. Proc. Natl Acad. Sci. USA 79, 2574–2578 (1982).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Handin, R. I., Lux, S. E. & Stossel, T. P. Blood: principles and practice of hematology (Lippincott Williams & Wilkins, Philadelphia, USA, 2003).

    Google Scholar 

  41. Maxfield, F. R. & McGraw, T. E. Endocytic recycling. Nature Rev. Mol. Cell Biol. 5, 121–132 (2004).

    CAS  Google Scholar 

  42. Wang, Y., Pennock, S. D., Chen, X., Kazlauskas, A. & Wang, Z. Platelet-derived growth factor receptor-mediated signal transduction from endosomes. J. Biol. Chem. 279, 8038–8046 (2004).

    CAS  PubMed  Google Scholar 

  43. Bomsztyk, K., Stanton, T. H., Smith, L. L., Rachie, N. A. & Dower, S. K. Properties of interleukin-1 and interferon-γ receptors in B lymphoid cell line. J. Biol. Chem. 264, 6052–6057 (1989).

    CAS  PubMed  Google Scholar 

  44. Alberts, B. Molecular biology of the cell 4th edn (Garland Science, New York, 2002).

    Google Scholar 

  45. Savinell, J. M., Lee, G. M. & Palsson, B. O. On the orders of magnitude of epigenic dynamics and monoclonal-antibody production. Bioproc. Eng. 4, 231–234 (1989).

    CAS  Google Scholar 

  46. Francis, K. & Palsson, B. O. Effective intercellular communication distances are determined by the relative time constants for cyto/chemokine secretion and diffusion. Proc. Natl Acad. Sci. USA 94, 12258–12262 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Ramirez-Weber, F. A. & Kornberg, T. B. Cytonemes: cellular processes that project to the principal signaling center in Drosophila imaginal discs. Cell 97, 599–607 (1999).

    CAS  PubMed  Google Scholar 

  48. Reed, J. L., Vo, T. D., Schilling, C. H. & Palsson, B. O. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4, R54 (2003).

    PubMed  PubMed Central  Google Scholar 

  49. van Drogen, F., Stucke, V. M., Jorritsma, G. & Peter, M. MAP kinase dynamics in response to pheromones in budding yeast. Nature Cell Biol. 3, 1051–1059 (2001).

    CAS  PubMed  Google Scholar 

  50. Kusari, A. B., Molina, D. M., Sabbagh, W. Jr., Lau, C. S. & Bardwell, L. A conserved protein interaction network involving the yeast MAP kinases Fus3 and Kss1. J. Cell Biol. 164, 267–277 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Wiley, H. S., Shvartsman, S. Y. & Lauffenburger, D. A. Computational modeling of the EGF-receptor system: a paradigm for systems biology. Trends Cell Biol. 13, 43–50 (2003). This review recounts the success of models of the EGF-receptor signalling system and argues for integrative computational and experimental approaches for dissecting signalling mechanisms.

    CAS  PubMed  Google Scholar 

  52. Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).

    CAS  PubMed  Google Scholar 

  53. Rives, A. W. & Galitski, T. Modular organization of cellular networks. Proc. Natl Acad. Sci. USA 100, 1128–1133 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402, C47–C52 (1999).

    CAS  PubMed  Google Scholar 

  55. Yi, T. M., Huang, Y., Simon, M. I. & Doyle, J. Robust perfect adaptation in bacterial chemotaxis through integral feedback control. Proc. Natl Acad. Sci. USA 97, 4649–4653 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. McAdams, H. H. & Shapiro, L. A bacterial cell-cycle regulatory network operating in time and space. Science 301, 1874–1877 (2003).

    CAS  PubMed  Google Scholar 

  57. Saucerman, J. J., Brunton, L. L., Michailova, A. P. & McCulloch, A. D. Modeling β-adrenergic control of cardiac myocyte contractility in silico. J. Biol. Chem. 278, 47997–48003 (2003).

    CAS  PubMed  Google Scholar 

  58. Lucas, P. C., McAllister-Lucas, L. M. & Nunez, G. NF-κB signaling in lymphocytes: a new cast of characters. J. Cell Sci. 117, 31–39 (2004).

    CAS  PubMed  Google Scholar 

  59. Cortassa, S., Aon, M. A., Marban, E., Winslow, R. L. & O'Rourke, B. An integrated model of cardiac mitochondrial energy metabolism and calcium dynamics. Biophys. J. 84, 2734–2755 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Spirin, V. & Mirny, L. A. Protein complexes and functional modules in molecular networks. Proc. Natl Acad. Sci. USA 100, 12123–12128 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Kauffman, K. J., Prakash, P. & Edwards, J. S. Advances in flux balance analysis. Curr. Opin. Biotechnol. 14, 491–496 (2003).

    CAS  PubMed  Google Scholar 

  62. Price, N. D., Papin, J. A., Schilling, C. H. & Palsson, B. O. Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol. 21, 162–169 (2003).

    CAS  PubMed  Google Scholar 

  63. Ren, B. et al. Genome-wide location and function of DNA binding proteins. Science 290, 2306–2309 (2000).

    CAS  PubMed  Google Scholar 

  64. Odom, D. T. et al. Control of pancreas and liver gene expression by HNF transcription factors. Science 303, 1378–1381 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Zhu, H. & Snyder, M. 'Omic' approaches for unraveling signaling networks. Curr. Opin. Cell Biol. 14, 173–179 (2002).

    CAS  PubMed  Google Scholar 

  66. Graves, P. R. & Haystead, T. A. A functional proteomics approach to signal transduction. Recent Prog. Horm. Res. 58, 1–24 (2003).

    CAS  PubMed  Google Scholar 

  67. von Mering, C. et al. Comparative assessment of large-scale data sets of protein–protein interactions. Nature 417, 399–403 (2002).

    CAS  PubMed  Google Scholar 

  68. Stagljar, I., Korostensky, C., Johnsson, N. & te Heesen, S. A genetic system based on split-ubiquitin for the analysis of interactions between membrane proteins in vivo. Proc. Natl Acad. Sci. USA 95, 5187–5192 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Aronheim, A., Zandi, E., Hennemann, H., Elledge, S. J. & Karin, M. Isolation of an AP-1 repressor by a novel method for detecting protein–protein interactions. Mol. Cell. Biol. 17, 3094–3102 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Gavin, A. C. et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002).

    CAS  PubMed  Google Scholar 

  71. Ho, Y. et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002).

    CAS  PubMed  Google Scholar 

  72. Stagljar, I. Finding partners: emerging protein interaction technologies applied to signaling networks. Sci. STKE pe56 (2003).

  73. Blagoev, B., Ong, S. E., Kratchmarova, I. & Mann, M. Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics. Nature Biotechnol. 22, 1139–1145 (2004).

    CAS  Google Scholar 

  74. Ideker, T. et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929–934 (2001).The authors present an approach for systematically interrogating a biochemical network.

    CAS  PubMed  Google Scholar 

  75. Lum, L. et al. Identification of Hedgehog pathway components by RNAi in Drosophila cultured cells. Science 299, 2039–2045 (2003).

    CAS  PubMed  Google Scholar 

  76. Brummelkamp, T. R., Nijman, S. M., Dirac, A. M. & Bernards, R. Loss of the cylindromatosis tumour suppressor inhibits apoptosis by activating NF-κB. Nature 424, 797–801 (2003).

    CAS  PubMed  Google Scholar 

  77. Zheng, L. et al. An approach to genomewide screens of expressed small interfering RNAs in mammalian cells. Proc. Natl Acad. Sci. USA 101, 135–140 (2004).

    CAS  PubMed  Google Scholar 

  78. Boutros, M. et al. Genome-wide RNAi analysis of growth and viability in Drosophila cells. Science 303, 832–835 (2004).

    CAS  PubMed  Google Scholar 

  79. Nielsen, U. B., Cardone, M. H., Sinskey, A. J., MacBeath, G. & Sorger, P. K. Profiling receptor tyrosine kinase activation by using Ab microarrays. Proc. Natl Acad. Sci. USA 100, 9330–9335 (2003).

    PubMed  PubMed Central  Google Scholar 

  80. Phair, R. D. & Misteli, T. Kinetic modelling approaches to in vivo imaging. Nature Rev. Mol. Cell Biol. 2, 898–907 (2001).

    CAS  Google Scholar 

  81. Meyer, T. & Teruel, M. N. Fluorescence imaging of signaling networks. Trends Cell Biol. 13, 101–106 (2003).

    CAS  PubMed  Google Scholar 

  82. Ding, D. Q. et al. Large-scale screening of intracellular protein localization in living fission yeast cells by the use of a GFP-fusion genomic DNA library. Genes Cells 5, 169–190 (2000).

    CAS  PubMed  Google Scholar 

  83. Huh, W. K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003).

    CAS  PubMed  Google Scholar 

  84. Martin-Fernandez, M., Clarke, D. T., Tobin, M. J., Jones, S. V. & Jones, G. R. Preformed oligomeric epidermal growth factor receptors undergo an ectodomain structure change during signaling. Biophys. J. 82, 2415–2427 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Bunemann, M., Frank, M. & Lohse, M. J. Gi protein activation in intact cells involves subunit rearrangement rather than dissociation. Proc. Natl Acad. Sci. USA 100, 16077–16082 (2003).

    PubMed  PubMed Central  Google Scholar 

  86. Sato, M., Ozawa, T., Inukai, K., Asano, T. & Umezawa, Y. Fluorescent indicators for imaging protein phosphorylation in single living cells. Nature Biotechnol. 20, 287–294 (2002).

    CAS  Google Scholar 

  87. Walhout, A. J. et al. Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline. Curr. Biol. 12, 1952–1958 (2002).

    CAS  PubMed  Google Scholar 

  88. Herrgard, M. J., Covert, M. W. & Palsson, B. O. Reconciling gene expression data with known genome-scale regulatory network structures. Genome Res. 13, 2423–2434 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Plavec, I. et al. Method for analyzing signaling networks in complex cellular systems. Proc. Natl Acad. Sci. USA 101, 1223–1228 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Tewari, M. et al. Systematic interactome mapping and genetic perturbation analysis of a C. elegans TGF-β signaling network. Mol. Cell 13, 469–482 (2004).

    CAS  PubMed  Google Scholar 

  91. Bouwmeester, T. et al. A physical and functional map of the human TNF-α/NF-κB signal transduction pathway. Nature Cell Biol. 6, 97–105 (2004). High-throughput experimental technologies are beginning to be used to interrogate signalling networks at a cellular level, as shown in this reference and in reference 87.

    CAS  PubMed  Google Scholar 

  92. Sambrano, G. R. et al. Unravelling the signal-transduction network in B lymphocytes. Nature 420, 708–710 (2002).

    CAS  PubMed  Google Scholar 

  93. Sambrano, G. R. et al. Navigating the signalling network in mouse cardiac myocytes. Nature 420, 712–714 (2002).

    CAS  PubMed  Google Scholar 

  94. Jeong, H., Mason, S. P., Barabasi, A. L. & Oltvai, Z. N. Lethality and centrality in protein networks. Nature 411, 41–42 (2001).

    CAS  PubMed  Google Scholar 

  95. Bu, D. et al. Topological structure analysis of the protein–protein interaction network in budding yeast. Nucleic Acids Res. 31, 2443–2450 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Schuster, S., Kholodenko, B. N. & Westerhoff, H. V. Cellular information transfer regarded from a stoichiometry and control analysis perspective. Biosystems 55, 73–81 (2000).

    CAS  PubMed  Google Scholar 

  97. Papin, J. A. & Palsson, B. O. The JAK–STAT signaling network in the human B-cell: an extreme signaling pathway analysis. Biophys. J. 87, 37–46 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Goodman, O. B. Jr. et al. Role of arrestins in G-protein-coupled receptor endocytosis. Adv. Pharmacol. 42, 429–433 (1998).

    CAS  PubMed  Google Scholar 

  99. Vuong, T. M. & Chabre, M. Deactivation kinetics of the transduction cascade of vision. Proc. Natl Acad. Sci. USA 88, 9813–9817 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Teruel, M. N. & Meyer, T. Translocation and reversible localization of signaling proteins: a dynamic future for signal transduction. Cell 103, 181–184 (2000).

    CAS  PubMed  Google Scholar 

  101. Lillemeier, B. F., Koster, M. & Kerr, I. M. STAT1 from the cell membrane to the DNA. EMBO J. 20, 2508–2517 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Elowitz, M. B., Surette, M. G., Wolf, P. E., Stock, J. B. & Leibler, S. Protein mobility in the cytoplasm of Escherichia coli. J. Bacteriol. 181, 197–203 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Swaminathan, R., Hoang, C. P. & Verkman, A. S. Photobleaching recovery and anisotropy decay of green fluorescent protein GFP–S65T in solution and cells: cytoplasmic viscosity probed by green fluorescent protein translational and rotational diffusion. Biophys. J. 72, 1900–1907 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Allan, V. Membrane traffic motors. FEBS Lett. 369, 101–106 (1995).

    CAS  PubMed  Google Scholar 

  105. Theurkauf, W. E. Premature microtubule-dependent cytoplasmic streaming in cappuccino and spire mutant oocytes. Science 265, 2093–2096 (1994).

    CAS  PubMed  Google Scholar 

  106. Stryer, L. Biochemistry (W. H. Freeman, New York, 1995).

    Google Scholar 

  107. Neves, S. R., Ram, P. T. & Iyengar, R. G protein pathways. Science 296, 1636–1639 (2002).

    CAS  PubMed  Google Scholar 

  108. Zubay, G. In vitro synthesis of protein in microbial systems. Annu. Rev. Genet. 7, 267–287 (1973).

    CAS  PubMed  Google Scholar 

  109. Rivett, A. J. Regulation of intracellular protein turnover: covalent modification as a mechanism of marking proteins for degradation. Curr. Top. Cell Regul. 28, 291–337 (1986).

    CAS  PubMed  Google Scholar 

  110. McAdams, H. H. & Arkin, A. Simulation of prokaryotic genetic circuits. Annu. Rev. Biophys. Biomol. Struct. 27, 199–224 (1998).

    CAS  PubMed  Google Scholar 

  111. Chang, D. Z., Wu, Z. & Ciardelli, T. L. A point mutation in interleukin-2 that alters ligand internalization. J. Biol. Chem. 271, 13349–13355 (1996).

    CAS  PubMed  Google Scholar 

  112. Ferguson, S. S. Evolving concepts in G protein-coupled receptor endocytosis: the role in receptor desensitization and signaling. Pharmacol. Rev. 53, 1–24 (2001).

    CAS  PubMed  Google Scholar 

  113. Jullien, J., Guili, V., Reichardt, L. F. & Rudkin, B. B. Molecular kinetics of nerve growth factor receptor trafficking and activation. J. Biol. Chem. 277, 38700–38708 (2002).

    CAS  PubMed  Google Scholar 

  114. Resat, H., Wiley, H. S. & Dixon, D. A. Probability-weighted dynamic Monte Carlo method for reaction kinetics simulations. J. Phys. Chem. B 105, 11026–11034 (2001).

    CAS  Google Scholar 

  115. Bailey, J. E. Complex biology with no parameters. Nature Biotechnol. 19, 503–504 (2001).

    CAS  Google Scholar 

  116. Bhalla, U. S. & Iyengar, R. Emergent properties of networks of biological signaling pathways. Science 283, 381–387 (1999). Describes some of the first large-scale analyses of signalling reactions.

    CAS  PubMed  Google Scholar 

  117. Hoffmann, A., Levchenko, A., Scott, M. L. & Baltimore, D. The IκB–NF-κB signaling module: temporal control and selective gene activation. Science 298, 1241–1245 (2002). Shows the powerful integration of mathematical modelling with experimental investigation.

    CAS  PubMed  Google Scholar 

  118. Lee, E., Salic, A., Kruger, R., Heinrich, R. & Kirschner, M. W. The roles of APC and Axin derived from experimental and theoretical analysis of the Wnt pathway. PLoS Biol. 1, 116–132 (2003).

    CAS  Google Scholar 

  119. Bartel, D. P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004).

    CAS  PubMed  Google Scholar 

  120. Forster, J., Famili, I., Fu, P., Palsson, B. B. & Nielsen, J. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13, 244–253 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Davidson, E. H. et al. A genomic regulatory network for development. Science 295, 1669–1678 (2002).

    CAS  PubMed  Google Scholar 

  122. Steffen, M., Petti, A., Aach, J., D'Haeseleer, P. & Church, G. Automated modelling of signal transduction networks. BMC Bioinformatics 3, 34 (2002).

    PubMed  PubMed Central  Google Scholar 

  123. Krauthammer, M. et al. Of truth and pathways: chasing bits of information through myriads of articles. Bioinformatics 18 (Suppl. 1), S249–S257 (2002).

    PubMed  Google Scholar 

  124. Cohen, P. Protein kinases — the major drug targets of the twenty-first century? Nature Rev. Drug Discov. 1, 309–315 (2002).

    CAS  Google Scholar 

  125. Dancey, J. & Sausville, E. A. Issues and progress with protein kinase inhibitors for cancer treatment. Nature Rev. Drug Discov. 2, 296–313 (2003).

    CAS  Google Scholar 

  126. Lazebnik, Y. Can a biologist fix a radio? — Or, what I learned while studying apoptosis. Cancer Cell 2, 179–182 (2002).

    CAS  PubMed  Google Scholar 

  127. Branden, C. & Tooze, J. Introduction to protein structure (Garland Pub., New York, USA, 1999).

    Google Scholar 

  128. Giannakakou, P. et al. p53 is associated with cellular microtubules and is transported to the nucleus by dynein. Nature Cell Biol. 2, 709–717 (2000).

    CAS  PubMed  Google Scholar 

  129. Devreotes, P. & Janetopoulos, C. Eukaryotic chemotaxis: distinctions between directional sensing and polarization. J. Biol. Chem. 278, 20445–20448 (2003).

    CAS  PubMed  Google Scholar 

  130. Heuser, J. E. & Salpeter, S. R. Organization of acetylcholine receptors in quick-frozen, deep-etched, and rotary-replicated Torpedo postsynaptic membrane. J. Cell Biol. 82, 150–173 (1979).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank the Whitaker Foundation for a Graduate Fellowship to J.P., and acknowledge grants from the National Institutes of Health to S.S. T.H. is a Frank and Else Schilling American Cancer Society Research Professor. B.O.P. is a member of the Scientific Advisory Board of Genomatica Inc. We would also like to acknowledge valuable input from A. Hoffmann, and thank T. Allen, S. Becker, N. Price and J. Reed for detailed feedback on the manuscript.

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Correspondence to Bernhard O. Palsson or Shankar Subramaniam.

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DATABASES

Entrez

CD44

NFKBIB

TTID

Saccharomyces genome database

Fus3

Swiss-Prot

GFP

IκBβ

JNK

RAS

SOS

TNFα

FURTHER INFORMATION

Alliance for Cellular Signaling

Bioinformatics and Computational Biology

Cell Migration Consortium

Genomatica

Human Proteomics Initiative

LIPID MAPS consortium

Molecular and Cellular Biology laboratory

Systems Biology Research Group

Glossary

NETWORK RECONSTRUCTION

The process of integrating different data sources to create a representation of the chemical events that underlie a biochemical reaction network.

ORDER OF MAGNITUDE

A simple, quantitative estimate of a parameter.

AUTOCRINE

Describing, or relating to, a cell that produces the ligands by which it is activated.

PARACRINE

Describing, or relating to, a regulatory cell that secretes an agonist into intercellular spaces from which it diffuses to a target cell other than the one that produces it.

ENDOCRINE

Describing, or relating to, a gland or group of cells that makes hormones and secretes them into the blood, lymph or intercellular fluid.

HELPER T CELL

A T cell that functions as an inducer of the effector cells for humoral and cell-mediated immunity. These cells recognize and bind to antigen.

PHAGOCYTOSIS

An actin-dependent process by which cells engulf external particulate material by extension and fusion of pseudopods.

G-PROTEIN-COUPLED RECEPTOR

(GPCR). A seven-helix membrane-spanning cell-surface receptor that signals through heterotrimeric GTP-binding and -hydrolysing G proteins to stimulate or inhibit the activity of a downstream enzyme.

MAST CELL

A type of leukocyte with large secretory granules that contain histamine and various protein mediators.

MEGAKARYOCYTES

Bone-marrow precursor cells that give rise to blood platelets. During differentiation, megakaryocytes become polyploid by endoreplication.

EXTRACELLULAR MATRIX

(ECM). The complex, multi-molecular material that surrounds cells. The ECM comprises a scaffold on which tissues are organized, it provides cellular microenvironments and it regulates various cellular functions.

SIGNALLING NODE

A highly connected compound in an intracellular signalling network.

SIGNALLING PATHWAY

A linear set of reactions that connects an input to an output in an intracellular signalling network.

SIGNALLING MODULE

An intuitive grouping of reactions from an intracellular signalling network that have a related function.

CONTEXTUAL SPECIFICITY

This takes into account the context in which a given signalling network property is observed — for example, splice variants of a particular protein might only exist in a cell when it is in a particular differentiated state.

L-TYPE CA2+ CHANNELS

A form of voltage-operated Ca2+ channel in cardiac muscle that has a high electrical threshold.

YEAST TWO-HYBRID ASSAY

A technique that is used to test whether two proteins physically interact with each other. One protein is fused to the GAL4 activation domain and the other to the GAL4 DNA-binding domain, and both fusion proteins are introduced into yeast. The expression of a GAL4-regulated reporter gene indicates that the two proteins physically interact.

GUANINE NUCLEOTIDE-EXCHANGE FACTOR

(GEF). A protein that facilitates the exchange of GDP (guanine diphosphate) for GTP (guanine triphosphate) in the nucleotide-binding pocket of a GTP-binding protein.

ICAT

(isotope coded affinity tag). ICAT probes have different masses, but are chemically identical. They incorporate a reactive cysteine, a biotin moiety, and eight deuteriums in place of eight hydrogens, and they are used to specifically label, by mass-difference, identical proteins in two separate samples for the identification and semiquantitative comparison of abundance.

SILAC

(stable isotope labelling by amino acids in culture). An experimental technique used to study hormone-activated protein complexes.

SH2 PROFILING

A technique based on the Far-Western assay that is used to identify SH2-binding domains in protein extracts.

TAIS

(target-assisted iterative screening). A method for screening protein products of a cDNA library that bind to a target protein.

RNAi

(RNA interference). A form of post-transcriptional gene silencing in which expression or transfection of dsRNA induces degradation — by nucleases — of the homologous endogenous transcripts. This mimics the effect of the reduction, or loss, of gene activity.

GREEN FLUORESCENT PROTEIN

(GFP). An autofluorescent protein that was originally identified in the jellyfish Aequorea victoria.

FRET

(fluorescence resonance energy transfer). The non-radiative transfer of energy from a donor fluorophore to an acceptor fluorophore that is typically <80 Å away. FRET will only occur between fluorophores in which the emission spectrum of the donor has a significant overlap with the excitation of the acceptor.

CLUSTERING ANALYSIS

An approach for identifying and grouping similar data points.

SPECTRAL ANALYSIS

A method derived from graph theory that describes high-level structures in complicated networks of relationships.

MONTE CARLO SAMPLING

An approach for choosing pseudo-random data points that represent the characteristics of a larger population or function.

BISTABLE BEHAVIOUR

A property in which there are two stable points of a dynamic system, which provides a sense of 'memory'.

WNT PROTEINS

A family of highly conserved secreted signalling molecules that regulate cell–cell interactions during embryogenesis.

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Papin, J., Hunter, T., Palsson, B. et al. Reconstruction of cellular signalling networks and analysis of their properties. Nat Rev Mol Cell Biol 6, 99–111 (2005). https://doi.org/10.1038/nrm1570

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