Rule-Based Modeling of Biochemical Systems with BioNetGen
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.
KeywordsComputational 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.
- 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.Gomperts, B. D., Kramer, I. M., and Tatham, P. E. R. (2003) Signal Transduction. Elsevier Academic Press, San Diego, CA.Google Scholar
- 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
- 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
- 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
- 45.Cohen, S. D., and Hindmarsh, A. C. (1996) CVODE, A Stiff/Nonstiff ODE Solver in C. Comp. Phys. 10, 138–143.Google Scholar
- 47.Berg, J. M., Tymoczko, J. L., and Stryer, L. (2006) Biochemistry. W. H. Freeman, New York.Google Scholar
- 48.Gross, J. L., and Yellen, J. (eds.) (2003) Handbook of Graph Theory.CRC Press, Boca Raton, FL.Google Scholar
- 49.McKay, B. D. (1981) Practical graph isomorphism. Congressus Numerantium 30, 45–87.Google Scholar
- 51.Lemons, N. and Hlavacek, W. S. private communication.Google Scholar
- 55.Lauffenburger, D. A. and Linderman, J. J. (1993) Receptors: Models for Binding, Trafficking, and Signalling. Oxford, New York, NY.Google Scholar