Rule-Based Modeling of Biochemical Systems with BioNetGen

Part of the Methods in Molecular Biology book series (MIMB, volume 500)


Rule-based modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate site-specific details about protein—protein interactions into a model for the dynamics of a signal-transduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of protein—protein interactions are difficult to specify and track with a conventional modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a rule-based model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in rule-based modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly large-scale models for other biochemical systems.


Computational systems biology Mathematical modeling Combinatorial complexity Software Formal languages Stochastic simulation Ordinary differential equations protein—protein interactions Signal transduction Metabolic networks 



Work on BioNetGen has been supported by NIH grants GM035556, RR18754, and GM76570 and DOE contract DE-AC52-06NA25396. J.R.F. also acknowledges support from the Department of Computational Biology at the University of Pittsburgh School of Medicine. Integration of BioNetGen into the Virtual Cell was supported by U54 RR022232 NIH-Roadmap grant for Technology Centers for Networks and Pathways. Special thanks to Byron Goldstein for the initial impetus that led to the development of BioNetGen and for his active and ongoing support. We thank the many people who have contributed to the development of BioNetGen and BioNetGen-compatible tools, including Jordan Atlas, Nikolay Borisov, Alexander Chistopolsky, Joshua Colvin, Thierry Emonet, Sarah Faeder, Leigh Fanning, Matthew Fricke, Bin Hu, Jeremy Kozdon, Mikhail Kravchenko, Nathan Lemons, Michael Monine, Fangping Mu, Ambarish Nag, Richard Posner, Amitabh Trehan, Robert Seletsky, Michael Sneddon, and Jin Yang. We also thank Gary An, Dipak Barua, Marc Birtwistle, James Cavenaugh, Ed Clarke, Vincent Danos, Jerome Feret, Andrew Finney, Walter Fontana, Leonard Harris, Jason Haugh, Michael Hucka, Sumit Jha, Jean Krivine, Chris Langmead, Paul Loriaux, Boris Kholodenko, Michael Saelim, Ed Stites, Ty Thomson, and Aileen Vandenberg for their helpful discussions and input. People contributing to the integration of BioNetGen with the Virtual Cell include James Schaff, Ion Moraru, Anuradha Lakshminarayana, Fei Gao, and Leslie Loew.


  1. 1.
    Blinov, M. L., Faeder, J. R., Goldstein, B., and Hlavacek, W. S. (2004) BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20, 3289–3291.PubMedCrossRefGoogle Scholar
  2. 2.
    Kholodenko, B. N. (2006) Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol. 7, 165–176.PubMedCrossRefGoogle Scholar
  3. 3.
    Aldridge, B. B., Burke, J. M., Lauffenburger, D. A., and Sorger, P. K. (2006) Physicochemical modelling of cell signalling pathways. Nat. Cell Biol. 8, 1195–1203.PubMedCrossRefGoogle Scholar
  4. 4.
    Dueber, J. E., Yeh, B. J., Bhattacharyya, R. P., and Lim, W. A. (2004) Rewiring cell signaling: the logic and plasticity of eukaryotic protein circuitry. Curr. Opin. Struct. Biol. 14, 690–699.PubMedCrossRefGoogle Scholar
  5. 5.
    Pawson, T. and Linding, R. (2005) Synthetic modular systems — Reverse engineering of signal transduction. FEBS Lett. 579, 1808–1814.PubMedCrossRefGoogle Scholar
  6. 6.
    Bashor, C. J., Helman, N. C., Yan, S., and Lim, W. A. (2008) Using engineered scaffold interactions to reshape MAP kinase pathway signaling dynamics. Science 319, 1539–1543.PubMedCrossRefGoogle Scholar
  7. 7.
    Hlavacek, W. S., Faeder, J. R., Blinov, M. L., Perelson, A. S., and Goldstein, B. (2003) The complexity of complexes in signal transduction. Biotechnol. Bioeng. 84, 783–794.PubMedCrossRefGoogle Scholar
  8. 8.
    Hlavacek, W. S., Faeder, J. R., Blinov, M. L., Posner, R. G., Hucka, M., and Fontana, W. (2006) Rules for modeling signal-transduction systems. Sci. STKE 2006, re6.Google Scholar
  9. 9.
    Gomperts, B. D., Kramer, I. M., and Tatham, P. E. R. (2003) Signal Transduction. Elsevier Academic Press, San Diego, CA.Google Scholar
  10. 10.
    Hunter, T. (2000) Signaling: 2000 and beyond. Cell 100, 113–127.PubMedCrossRefGoogle Scholar
  11. 11.
    Cambier, J. C. (1995) Antigen and Fc receptor signaling: The awesome power of the immunoreceptor tyrosine-based activation motif (ITAM). J. Immunol. 155, 3281–3285.PubMedGoogle Scholar
  12. 12.
    Pawson, T. and Nash, P. (2003) Assembly of cell regulatory systems through protein interaction domains. Science 300, 445–452.PubMedCrossRefGoogle Scholar
  13. 13.
    Pawson, T. (2004) Specificity in signal transduction: From phosphotyrosine-SH2 domain interactions to complex cellular systems. Cell 116, 191–203.PubMedCrossRefGoogle Scholar
  14. 14.
    Seet, B. T., Dikic, I., Zhou, M. M., and Pawson, T. (2006) Reading protein modifications with interaction domains. Nat. Rev. Mol. Cell Biol. 7, 473–483.PubMedCrossRefGoogle Scholar
  15. 15.
    Mathivanan, S., Periaswamy, B., Gandhi, T. K. B., Kandasamy, K., Suresh, S., Mohmood, R., Ramachandra, Y. L., and Pandey, A. (2006) An evaluation of human protein-protein interaction data in the public domain. BMC Bioinformatics 7, S19.PubMedCrossRefGoogle Scholar
  16. 16.
    Mathivanan, S., Ahmed, M., Ahn, N. G., Alexandre, H., Amanchy, R., Andrews, P. C., Bader, J. S., Balgley, B. M., Bantscheff, M., Bennett, K. L., et al. (2008) Human Proteinpedia enables sharing of human protein data. Nat. Biotechnol. 26, 164–167.PubMedCrossRefGoogle Scholar
  17. 17.
    Ong, S. E. and Mann, M. (2005) Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 1, 252–262.PubMedCrossRefGoogle Scholar
  18. 18.
    Olsen, J. V., Blagoev, B., Gnad, F., Macek, B., Kumar, C., Mortensen, P., and Mann, M. (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635–648.PubMedCrossRefGoogle Scholar
  19. 19.
    Kholodenko, B. N., Demin, O. V., Moehren, G., and Hoek, J. B. (1999) Quantification of short term signaling by the epidermal growth factor receptor. J. Biol. Chem. 274, 30169–30181.PubMedCrossRefGoogle Scholar
  20. 20.
    Schoeberl, B., Eichler-Jonsson, C., Gilles, E. D., and Muller, G. (2002) Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat. Biotechnol. 20, 370–375.PubMedCrossRefGoogle Scholar
  21. 21.
    Morton-Firth, C. J. and Bray, D. (1998) Predicting temporal fluctuations in an intracellular signalling pathway. J. Theor. Biol. 192, 117–128.PubMedCrossRefGoogle Scholar
  22. 22.
    Endy, D. and Brent, R. (2001) Modelling cellular behaviour. Nature 409, 391–395.PubMedCrossRefGoogle Scholar
  23. 23.
    Jorissen, R. N., Walker, F., Pouliot, N., Garrett, T. P. J., Ward, C. W., and Burgess, A. W. (2003) Epidermal growth factor receptor: Mechanisms of activation and signalling. Exp. Cell Res. 284, 31–53.PubMedCrossRefGoogle Scholar
  24. 24.
    Danos, V. and Laneve, C. (2004) Formal molecular biology. Theor. Comput. Sci. 325, 69–110.CrossRefGoogle Scholar
  25. 25.
    Faeder, J. R., Blinov, M. L., and Hlavacek, W. S. (2005) Graphical rule-based representation of signal-transduction networks, in SAC '05: Proc. ACM Symp. Appl. Computing, ACM, New York, NY, pp. 133–140.Google Scholar
  26. 26.
    Blinov, M. L., Yang, J., Faeder, J. R., and Hlavacek, W. S. (2006) Graph theory for rule-based modeling of biochemical networks. Lect. Notes Comput. Sci. 4230, 89–106.CrossRefGoogle Scholar
  27. 27.
    Danos, V., Feret, J., Fontana, W., Harmer, R., and Krivine, J. (2007) Rule-based modelling of cellular signalling. Lect. Notes Comput. Sci. 4703, 17–41.CrossRefGoogle Scholar
  28. 28.
    Faeder, J. R., Blinov, M. L., Goldstein, B., and Hlavacek, W. S. (2005) Rule-based modeling of biochemical networks. Complexity 10, 22–41.CrossRefGoogle Scholar
  29. 29.
    Lok, L. and Brent, R. (2005) Automatic generation of cellular networks with Moleculizer 1.0. Nat. Biotechnol. 23, 131–36.PubMedCrossRefGoogle Scholar
  30. 30.
    Danos, V., Feret, J., Fontana, W., and Krivine, J. (2007) Scalable simulation of cellular signalling networks. Lect. Notes Comput. Sci. 4807, 139–157.CrossRefGoogle Scholar
  31. 31.
    Yang, J., Monine, M. I., Faeder, J. R., and Hlavacek, W. S. (2007) Kinetic Monte Carlo method for rule-based modeling of biochemical networks. arXiv:0712.3773.Google Scholar
  32. 32.
    Goldstein, B., Faeder, J. R., Hlavacek, W. S., Blinov, M. L., Redondo, A., and Wofsy, C. (2002) Modeling the early signaling events mediated by FceRI. Mol. Immunol. 38, 1213–1219.PubMedCrossRefGoogle Scholar
  33. 33.
    Faeder, J. R., Hlavacek, W. S., Reischl, I., Blinov, M. L., Metzger, H., Redondo, A., Wofsy, C., and Goldstein, B. (2003) Investigation of early events in FceRI-mediated signaling using a detailed mathematical model. J. Immunol. 170, 3769–3781.PubMedGoogle Scholar
  34. 34.
    Faeder, J. R., Blinov, M. L., Goldstein, B., and Hlavacek, W. S. (2005) Combinatorial complexity and dynamical restriction of network flows in signal transduction. Syst. Biol. 2, 5–15.CrossRefGoogle Scholar
  35. 35.
    Blinov, M. L., Faeder, J. R., Goldstein, B., and Hlavacek, W. S. (2006) A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity. BioSystems 83, 136–151.PubMedCrossRefGoogle Scholar
  36. 36.
    Barua, D., Faeder, J. R., and Haugh, J. M. (2007) Structure-based kinetic models of modular signaling protein function: focus on Shp2. Biophys. J. 92, 2290–2300.PubMedCrossRefGoogle Scholar
  37. 37.
    Barua, D., Faeder, J. R., and Haugh, J. M. (2008) Computational models of tandem Src homology 2 domain interactions and application to phosphoinositide 3-kinase. J. Biol. Chem. 283, 7338–7345.PubMedCrossRefGoogle Scholar
  38. 38.
    Mu, F. P., Williams, R. F., Unkefer, C. J., Unkefer, P. J., Faeder, J. R., and Hlavacek, W. S. (2007) Carbon-fate maps for metabolic reactions. Bioinformatics 23, 3193–3199.PubMedCrossRefGoogle Scholar
  39. 39.
    Rubenstein, R., Gray, P. C., Cleland, T. J., Piltch, M. S., Hlavacek, W. S., Roberts, R. M., Ambrosiano, J., and Kim, J. I. (2007) Dynamics of the nucleated polymerization model of prion replication. Biophys. Chem. 125, 360–367.PubMedCrossRefGoogle Scholar
  40. 40.
    Gillespie, D. T. (1976) A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comp. Phys. 22, 403–434.CrossRefGoogle Scholar
  41. 41.
    Gillespie, D. T. (1977) Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361.CrossRefGoogle Scholar
  42. 42.
    Gillespie, D. T. (2007) Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem. 58, 35–55.PubMedCrossRefGoogle Scholar
  43. 43.
    Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., Arkin, A. P., Bornstein, B. J., Bray, D., Cornish-Bowden, A., et al. (2003) The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531.PubMedCrossRefGoogle Scholar
  44. 44.
    Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., and Kummer, U. (2006) COPASI--a COmplex PAthway SImulator. Bioinformatics 22, 3067–3074.PubMedCrossRefGoogle Scholar
  45. 45.
    Cohen, S. D., and Hindmarsh, A. C. (1996) CVODE, A Stiff/Nonstiff ODE Solver in C. Comp. Phys. 10, 138–143.Google Scholar
  46. 46.
    Hindmarsh, A. C., Brown, P. N., Grant, K. E., Lee, S. L., Serban, R., Shumaker, D. E., and Woodward, C. S. (2005) SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers. ACM Trans. Math. Softw. 31, 363–96.CrossRefGoogle Scholar
  47. 47.
    Berg, J. M., Tymoczko, J. L., and Stryer, L. (2006) Biochemistry. W. H. Freeman, New York.Google Scholar
  48. 48.
    Gross, J. L., and Yellen, J. (eds.) (2003) Handbook of Graph Theory.CRC Press, Boca Raton, FL.Google Scholar
  49. 49.
    McKay, B. D. (1981) Practical graph isomorphism. Congressus Numerantium 30, 45–87.Google Scholar
  50. 50.
    Ullmann, J. R. (1976) An algorithm for subgraph isomorphism. J. ACM 23, 31–42.CrossRefGoogle Scholar
  51. 51.
    Lemons, N. and Hlavacek, W. S. private communication.Google Scholar
  52. 52.
    Borisov, N. M., Markevich, N. I., Hoek, J. B., and Kholodenko, B. N. (2005) Signaling through receptors and scaffolds: Independent interactions reduce combinatorial complexity. Biophys. J. 89, 951–966.PubMedCrossRefGoogle Scholar
  53. 53.
    Borisov, N. M., Markevich, N. I., Hoek, J. B., and Kholodenko, B. N. (2006) Trading the micro-world of combinatorial complexity for the macro-world of protein interaction domains. BioSystems 83, 152–166.PubMedCrossRefGoogle Scholar
  54. 54.
    Borisov, N. M., Chistopolsky, A. S., Kholodenko, B. N., and Faeder, J. R. (2008) Domain-oriented reduction of rule-based network models IET Syst. Biol. 2, 342–351.PubMedCrossRefGoogle Scholar
  55. 55.
    Lauffenburger, D. A. and Linderman, J. J. (1993) Receptors: Models for Binding, Trafficking, and Signalling. Oxford, New York, NY.Google Scholar
  56. 56.
    Posner, R. G., Wofsy, C., and Goldstein, B. (1995) The kinetics of bivalent ligand-bivalent receptor aggregation: Ring formation and the breakdown of the equivalent site approximation. Math. Biosci. 126, 171–190.PubMedCrossRefGoogle Scholar
  57. 57.
    Pollard, T. D. and Borisy, G. G. (2003) Cellular motility driven by assembly and disassembly of actin filaments. Cell 112, 453–465.PubMedCrossRefGoogle Scholar
  58. 58.
    Koschorreck, M., Conzelmann, H., Ebert, S., Ederer, M., and Gilles, E. D. (2007) Reduced modeling of signal transduction — A modular approach. BMC Bioinformatics 8, 336.PubMedCrossRefGoogle Scholar
  59. 59.
    Heath, J., Kwiatkowska, M., Norman, G., Parker, D., and Tymchyshyn, O. (2007) Probabilistic model checking of complex biological pathways. Lect. Notes Comput. Sci. 4210, 32–47.CrossRefGoogle Scholar

Copyright information

© Humana Press 2009

Authors and Affiliations

  1. 1.Department of Computational BiologyUniversity of Pittsburgh School of MedicinePittsburgh

Personalised recommendations