Rule-Based Modeling of Signal Transduction: A Primer

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 880)

Abstract

Biological cells accomplish their physiological functions using interconnected networks of genes, proteins, and other biomolecules. Most interactions in biological signaling networks, such as bimolecular association or covalent modification, can be modeled in a physically realistic manner using elementary reaction kinetics. However, the size and combinatorial complexity of such reaction networks have hindered such a mechanistic approach, leading many to conclude that it is premature and to adopt alternative statistical or phenomenological approaches. The recent development of rule-based modeling languages, such as BioNetGen (BNG) and Kappa, enables the precise and succinct encoding of large reaction networks. Coupled with complementary advances in simulation methods, these languages circumvent the combinatorial barrier and allow mechanistic modeling on a much larger scale than previously possible. These languages are also intuitive to the biologist and accessible to the novice modeler. In this chapter, we provide a self-contained tutorial on modeling signal transduction networks using the BNG Language and related software tools. We review the basic syntax of the language and show how biochemical knowledge can be articulated using reaction rules, which can be used to capture a broad range of biochemical and biophysical phenomena in a concise and modular way. A model of ligand-activated receptor dimerization is examined, with a detailed treatment of each step of the modeling process. Sections discussing modeling theory, implicit and explicit model assumptions, and model parameterization are included, with special focus on retaining biophysical realism and avoiding common pitfalls. We also discuss the more advanced case of compartmental modeling using the compartmental extension to BioNetGen. In addition, we provide a comprehensive set of example reaction rules that cover the various aspects of signal transduction, from signaling at the membrane to gene regulation. The reader can modify these reaction rules to model their own systems of interest.

Key words

Computational systems biology Mathematical modeling Combinatorial complexity Signal transduction Formal languages BioNetGen Stochastic simulation Ordinary differential equations Network-free simulation Compartmental modeling Compartmental modeling Protein–protein interactions Signal transduction Kinase cascades Signaling diagrams 

References

  1. 1.
    Kholodenko BN, Hancock JF, Kolch W (2010) Signalling ballet in space and time. Nat Rev Mol Cell Biol 11:414–426PubMedCrossRefGoogle Scholar
  2. 2.
    Kholodenko BN (2006) Cell-signalling dynamics in time and space. Nat Rev Mol Cell Biol 7:165–176PubMedCrossRefGoogle Scholar
  3. 3.
    Scott JD, Pawson T (2009) Cell signaling in space and time: where proteins come together and when they're apart. Science (New York, NY) 326:1220–1224Google Scholar
  4. 4.
    Natarajan M, Lin K-M, Hsueh RC, Sternweis PC, Ranganathan R (2006) A global analysis of cross-talk in a mammalian cellular signalling network. Nat Cell Biol 8:571–580PubMedCrossRefGoogle Scholar
  5. 5.
    Hecker M, Lambeck S, Toepfer S, van Someren E, Guthke R (2009) Gene regulatory network inference: data integration in dynamic models – a review. Biosystems 96:86–103PubMedCrossRefGoogle Scholar
  6. 6.
    Hlavacek WS, Faeder JR (2009) The complexity of cell signaling and the need for a new mechanics. Sci Signal 2:46CrossRefGoogle Scholar
  7. 7.
    Hlavacek W, Faeder J, Blinov M (2006) Rules for modeling signal-transduction systems. Sci STKE 344:6Google Scholar
  8. 8.
    Bachman JA, Sorger P (2011) New approaches to modeling complex biochemistry. Nat Methods 8:130–131PubMedCrossRefGoogle Scholar
  9. 9.
    Faeder J, Blinov M, Hlavacek W (2009) Rule-based modeling of biochemical systems with BioNetGen. Methods Mol Biol 500:113–167PubMedCrossRefGoogle Scholar
  10. 10.
    Blinov ML, Faeder JR, Goldstein B, Hlavacek WS (2004) BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20:3289–3291PubMedCrossRefGoogle Scholar
  11. 11.
    Danos V, Laneve C (2004) Formal molecular biology. Theor Comput Sci 325:69–110CrossRefGoogle Scholar
  12. 12.
    Feret J, Danos V, Krivine J, Harmer R, Fontana W (2009) Internal coarse-graining of molecular systems. Proc Natl Acad Sci USA 106:6453–6458PubMedCrossRefGoogle Scholar
  13. 13.
    Haugh JM, Lauffenburger DA (1997) Physical modulation of intracellular signaling processes by locational regulation. Biophys J 72:2014–2031PubMedCrossRefGoogle Scholar
  14. 14.
    Michaelis L, Menten M (1913) Die kinetik der invertinwirkung. Biochem Z 49:333–369Google Scholar
  15. 15.
    Briggs G, Haldane J (1925) A note on the kinetics of enzyme action. Biochem J 19:338PubMedGoogle Scholar
  16. 16.
    Cornish-Bowden A (2004) Fundamentals of enzyme kinetics, 3rd edn. Portland Press, LondonGoogle Scholar
  17. 17.
    Chen WW, Niepel M, Sorger PK (2010) Classic and contemporary approaches to modeling biochemical reactions. Gene Dev 24:1861–1875PubMedCrossRefGoogle Scholar
  18. 18.
    Tracy TS, Hummel MA (2004) Modeling kinetic data from in vitro drug metabolism enzyme experiments. Drug Metab Rev 36:231–242PubMedCrossRefGoogle Scholar
  19. 19.
    Sabouri-Ghomi M, Ciliberto A, Kar S, Novak B, Tyson JJ (2008) Antagonism and bistability in protein interaction networks. J Theor Biol 250:209–218PubMedCrossRefGoogle Scholar
  20. 20.
    Sneddon MW, Faeder JR, Emonet T (2011) Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods 8:177–183PubMedCrossRefGoogle Scholar
  21. 21.
    Chen WW, Schoeberl B, Jasper PJ, Niepel M, Nielsen UB, Lauffenburger DA, Sorger PK (2009) Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. Mol Syst Biol 5:239PubMedGoogle Scholar
  22. 22.
    Barik D, Baumann WT, Paul MR, Novak B, Tyson JJ (2010) A model of yeast cell-cycle regulation based on multisite phosphorylation. Mol Syst Biol 6:405PubMedCrossRefGoogle Scholar
  23. 23.
    Hänggi P (2002) Stochastic resonance in biology how noise can enhance detection of weak signals and help improve biological information processing. Chemphyschem 3:285–290PubMedCrossRefGoogle Scholar
  24. 24.
    Gillespie D (1976) A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 22:403–434CrossRefGoogle Scholar
  25. 25.
    Yang J, Monine MI, Faeder JR, Hlavacek WS (2008) Kinetic Monte Carlo method for rule-based modeling of biochemical networks. Phys Rev E 78:031910CrossRefGoogle Scholar
  26. 26.
    Borisov N, Chistopolsky A, Faeder J, Kholodenko B (2008) Domain-oriented reduction of rule-based network models. IET Syst Biol 2:342–351PubMedCrossRefGoogle Scholar
  27. 27.
    Conzelmann H, Saez-Rodriguez J, Sauter T, Kholodenko BN, Gilles ED (2006) A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks. BMC Bioinformatics 7:34PubMedCrossRefGoogle Scholar
  28. 28.
    Lok L, Brent R (2005) Automatic generation of cellular reaction networks with Moleculizer 1.0. Nat Biotechnol 23:131–136PubMedCrossRefGoogle Scholar
  29. 29.
    Faeder JR, Blinov M, Goldstein B, Hlavacek W (2005) Rule-based modeling of biochemical networks. Complexity 10:22–41Google Scholar
  30. 30.
    Danos V, Feret J, Fontana W, Krivine J (2007) Scalable simulation of cellular signaling networks. Lect Notes Comput Sci 4807:139–157CrossRefGoogle Scholar
  31. 31.
    Le Novére N, Shimizu TS (2001) STOCHSIM: modelling of stochastic biomolecular processes. Bioinformatics 17:575–576PubMedCrossRefGoogle Scholar
  32. 32.
    Colvin J, Monine MI, Faeder JR, Hlavacek WS, Hoff DDV, Posner RG (2009) Simulation of large-scale rule-based models. Bioinformatics 25:910–917PubMedCrossRefGoogle Scholar
  33. 33.
    Colvin J, Monine MI, Gutenkunst RN, Hlavacek WS, Hoff DDV, Posner RG (2010) RuleMonkey: software for stochastic simulation of rule-based models. BMC Bioinformatics 11:404PubMedCrossRefGoogle Scholar
  34. 34.
    Walker F, Orchard SG, Jorissen RN, Hall NE, Zhang H-H, Hoyne PA, Adams TE, Johns TG, Ward C, Garrett TPJ, Zhu H-J, Nerrie M, Scott AM, Nice EC, Burgess AW (2004) CR1/CR2 interactions modulate the functions of the cell surface epidermal growth factor receptor. J Biol Chem 279:22387–22398PubMedCrossRefGoogle Scholar
  35. 35.
    Jorissen RN, Walker F, Pouliot N, Garrett TPJ, Ward CW, Burgess AW (2003) Epidermal growth factor receptor: mechanisms of activation and signalling. Exp Cell Res 284:31–53PubMedCrossRefGoogle Scholar
  36. 36.
    Zhang X, Gureasko J, Shen K, Cole PA, Kuriyan J (2006) An allosteric mechanism for activation of the kinase domain of epidermal growth factor receptor. Cell 125:1137–1149PubMedCrossRefGoogle Scholar
  37. 37.
    Tao R, Maruyama I (2008) All EGF (ErbB) receptors have preformed homo-and heterodimeric structures in living cells. J Cell Sci 121:3207–3217PubMedCrossRefGoogle Scholar
  38. 38.
    Kholodenko BN, Demin OV, Moehren G, Hoek JB (1999) Quantification of short term signaling by the epidermal growth factor receptor. J Biol Chem 274:30169–30181PubMedCrossRefGoogle Scholar
  39. 39.
    Schoeberl B, Eichler-Jonsson C, Gilles ED, Müller G (2002) Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol 20:370–375PubMedCrossRefGoogle Scholar
  40. 40.
    Wofsy C, Goldstein B, Lund K, Wiley HS (1992) Implications of epidermal growth factor (EGF) induced egf receptor aggregation. Biophys J 63:98–110PubMedCrossRefGoogle Scholar
  41. 41.
    Ollivier JF, Shahrezaei V, Swain PS (2010) Scalable rule-based modelling of allosteric proteins and biochemical networks. PLoS Comput Biol 6:e1000975PubMedCrossRefGoogle Scholar
  42. 42.
    Macdonald JL, Pike LJ (2008) Heterogeneity in EGF-binding affinities arises from negative cooperativity in an aggregating system. Proc Natl Acad Sci USA 105:112–117PubMedCrossRefGoogle Scholar
  43. 43.
    Alvarado D, Klein DE, Lemmon MA (2010) Structural basis for negative cooperativity in growth factor binding to an EGF receptor. Cell 142:568–579PubMedCrossRefGoogle Scholar
  44. 44.
    Harris LA, Hogg JS, Faeder JR (2009) Compartmental rule-based modeling of biochemical systems. Proceedings of the 2009 winter simulation conference, 13–16 Dec 2009, Austin TX, USAGoogle Scholar
  45. 45.
    Saltelli A, Ratto M, Tarantola S, Campolongo F (2005) Sensitivity analysis for chemical models. Chem Rev 105:2811–2827PubMedCrossRefGoogle Scholar
  46. 46.
    Seewöster T, Lehmann J (1997) Cell size distribution as a parameter for the predetermination of exponential growth during repeated batch cultivation of CHO cells. Biotechnol Bioeng 55:793–797PubMedCrossRefGoogle Scholar
  47. 47.
    Swanson JA, Lee M, Knapp PE (1991) Cellular dimensions affecting the nucleocytoplasmic volume ratio. J Cell Biol 115:941–948PubMedCrossRefGoogle Scholar
  48. 48.
    Lipniacki T, Puszynski K, Paszek P, Brasier AR, Kimmel M (2007) Single TNFalpha trimers mediating NF-kappaB activation: stochastic robustness of NF-kappaB signaling. BMC Bioinformatics 8:376PubMedCrossRefGoogle Scholar
  49. 49.
    Ghaemmaghami S, Huh W-K, Bower K, Howson RW, Belle A, Dephoure N, O'Shea EK, Weissman JS (2003) Global analysis of protein expression in yeast. Nature 425:737–741PubMedCrossRefGoogle Scholar
  50. 50.
    Lee B, LeDuc PR, Schwartz R (2008) Stochastic off-lattice modeling of molecular self-assembly in crowded environments by Green’s function reaction dynamics. Phys Rev E Stat 78:031911CrossRefGoogle Scholar
  51. 51.
    Kim JS, Yethiraj A (2010) Crowding effects on association reactions at membranes. Biophys J 98:951–958PubMedCrossRefGoogle Scholar
  52. 52.
    Gabdoulline RR, Wade RC (2002) Biomolecular diffusional association. Curr Opin Struct Biol 12:204–213PubMedCrossRefGoogle Scholar
  53. 53.
    Alberts B, Bray D, Lewis J, Raff M, Roberts K, Watson JD (1994) Molecular biology of the cell, 3rd edn. Garland Publishing, New YorkGoogle Scholar
  54. 54.
    Hlavacek WS, Faeder JR, Blinov ML, Perelson AS, Goldstein B (2003) The complexity of complexes in signal transduction. Biotechnol Bioeng 84:783–794PubMedCrossRefGoogle Scholar
  55. 55.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242PubMedCrossRefGoogle Scholar
  56. 56.
    Consortium U (2010) The universal protein resource (UniProt) in 2010. Nucleic Acids Res 38:D142–D148CrossRefGoogle Scholar
  57. 57.
    Schulze WX, Deng L, Mann M (2005) Phosphotyrosine interactome of the ErbB-receptor kinase family. Mol Syst Biol 1:2005.0008Google Scholar
  58. 58.
    Gu H, Neel BG (2003) The “Gab” in signal transduction. Trends Cell Biol 13:122–130PubMedCrossRefGoogle Scholar
  59. 59.
    Ravichandran KS (2001) Signaling via Shc family adapter proteins. Oncogene 20:6322–6330PubMedCrossRefGoogle Scholar
  60. 60.
    Neel B, Gu H, Pao L (2003) The ‘Shp’ing news: SH2 domain-containing tyrosine phosphatases in cell signaling. Trends Biochem Sci 28:284–293PubMedCrossRefGoogle Scholar
  61. 61.
    Das J, Ho M, Zikherman J, Govern C, Yang M, Weiss A, Chakraborty AK, Roose JP (2009) Digital signaling and hysteresis characterize ras activation in lymphoid cells. Cell 136:337–351PubMedCrossRefGoogle Scholar
  62. 62.
    Langlois WJ, Sasaoka T, Saltiel AR, Olefsky JM (1995) Negative feedback regulation and desensitization of insulin- and epidermal growth factor-stimulated p21ras activation. J Biol Chem 270:25320–25323PubMedCrossRefGoogle Scholar
  63. 63.
    Seminario M-C, Precht P, Bunnell SC, Warren SE, Morris CM, Taub D, Wange RL (2004) PTEN permits acute increases in D3-phosphoinositide levels following TCR stimulation but inhibits distal signaling events by reducing the basal activity of Akt. Eur J Immunol 34:3165–3175PubMedCrossRefGoogle Scholar
  64. 64.
    Terai K, Matsuda M (2005) Ras binding opens c-Raf to expose the docking site for mitogen-activated protein kinase kinase. EMBO Rep 6:251–255PubMedCrossRefGoogle Scholar
  65. 65.
    Huang W, Kessler DS, Erikson RL (1995) Biochemical and biological analysis of Mek1 phosphorylation site mutants. Mol Biol Cell 6:237–245PubMedGoogle Scholar
  66. 66.
    Grethe S, Pцrn-Ares MI (2006) p38 MAPK regulates phosphorylation of Bad via PP2A-dependent suppression of the MEK1/2-ERK1/2 survival pathway in TNF-alpha induced endothelial apoptosis. Cell Signal 18:531–540PubMedCrossRefGoogle Scholar
  67. 67.
    Kamioka Y, Yasuda S, Fujita Y, Aoki K, Matsuda M (2010) Multiple decisive phosphorylation sites for the negative feedback regulation of SOS1 via ERK. J Biol Chem 285:33540–33548PubMedCrossRefGoogle Scholar
  68. 68.
    Caunt CJ, Rivers CA, Conway-Campbell BL, Norman MR, McArdle CA (2008) Epidermal growth factor receptor and protein kinase C signaling to ERK2: spatiotemporal regulation of ERK2 by dual specificity phosphatases. J Biol Chem 283:6241–6252PubMedCrossRefGoogle Scholar
  69. 69.
    Cobb MH, Goldsmith EJ (2000) Dimerization in MAP-kinase signaling. Trends Biochem Sci 25:7–9PubMedCrossRefGoogle Scholar
  70. 70.
    Chang L, Karin M (2001) Mammalian MAP kinase signalling cascades. Nature 410:37–40PubMedCrossRefGoogle Scholar
  71. 71.
    Shaulian E, Karin M (2002) AP-1 as a regulator of cell life and death. Nat Cell Biol 4:E131–E136PubMedCrossRefGoogle Scholar
  72. 72.
    Weiss J (1997) The Hill equation revisited: uses and misuses. FASEB J 11:835–841PubMedGoogle Scholar
  73. 73.
    Sauro HM, Kholodenko BN (2004) Quantitative analysis of signaling networks. Prog Biophys Mol Biol 86:5–43PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Computational and Systems BiologyUniversity of Pittsburgh School of MedicinePittsburghUSA

Personalised recommendations