BioNetGen References

The most authoritative description of the current version of BioNetGen is given in the BioNetGen Tutorial. Links shown in red are available on our BioNetWiki site. If you would like to use this site, please send us an email indicating a preferred login name and we will set up an account for you.

Reviews

• W. S. Hlavacek, J. R. Faeder, M. L. Blinov, R. G. Posner, M. Hucka, and W. Fontana (2006) Rules for modeling signal-transduction systems. Sci. STKE., 2006, re6. [1]

A comprehensive overview of rule-based modeling along with examples and some description of BioNetGen. Rule-based modeling is also discussed in other recent reviews: Kholodenko (2006; PMID 16482094) and Aldridge et al. (2006; PMID 17060902).

• W. S. Hlavacek, J. R. Faeder, M. L. Blinov, A. S. Perelson, B. Goldstein (2003) The complexity of complexes in signal transduction. Biotechnol. Bioeng., 84, 783-794. [2]

Raises the problem of combinatorial complexity in signaling and proposes a rule-based modeling approach as a possible solution.

BioNetGen1

The following papers describe BioNetGen1, which was based on a term-rewriting approach that has limited ability to represent the topology of complexes. BioNetGen1 is related to StochSim and BIOCHAM.

• M. L. Blinov, J. R. Faeder, B. Goldstein, and W. S. Hlavacek (2004) BioNetGen: Software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics, 20, 3289-3292. [3]
• J. R. Faeder, M. L. Blinov, B. Goldstein and W. S. Hlavacek (2005) Rule-based modeling of biochemical networks. Complexity, 10, 22-41. [4]

More details about methodology can be found here.

BioNetGen2

This paper introduces the graphical and graph-based notation behind BioNetGen2 and provides examples.

• J. R. Faeder, M. L. Blinov, and W. S. Hlavacek (2005) Graphical rule-based representation of signal-transduction networks. In Proc. 2005 ACM Symp. Appl. Computing, Ed. L. M. Liebrock, ACM Press, pp. 133-140. [5]

This paper provides a more formal description of the graph-based notation and the algorithms used to perform network generation and on-the-fly simulation.

• M. L. Blinov, J. Yang, J. R. Faeder and W. S. Hlavacek (2006) Graph theory for rule-based modeling of biochemical networks. Lect. Notes Comput. Sci., 4230, 89-106. [6]

As it turns out, the BioNetGen model-specification language (BNGL) is related to the κ-calculus of Danos & Laneve (2004; preprint pdf), which can be reduced to π-calculus, a minimalist language for describing concurrent systems that is well known in computer science.

Applications

Immunoreceptors

• J. R. Faeder, W. S. Hlavacek, A. Redondo, C. Wofsy, M. Blinov, and B. Goldstein (2001) A Detailed Kinetic Model of Immunoreceptor Signaling. In Proceedings of the Second International Conference on Systems Biology, Eds. T.-M. Yi, M. Hucka, M. Morohashi, H. Kitano, p. 17. [7]
• B. Goldstein, J. R. Faeder, W. S. Hlavacek, M. L. Blinov, A. Redondo, and C. Wofsy (2002) Modeling the early signaling events mediated by aggregation of FcεRI. Mol. Immunol., 38, 1213-1219. [8]
• J. R. Faeder, W. S. Hlavacek, I. Reischl, M. L. Blinov, H. Metzger, A. Redondo, C. Wofsy, and B. Goldstein (2003) Investigation of early events in FcεRI-mediated signaling using a detailed mathematical model. J. Immunol., 170, 3769-3781. [9]

These papers describe the idea of rule-based modeling in the context of signaling through the high affinity receptor for IgE (FcεRI). The last paper is the best one and provides the most details about the problem-specific rule-based approach we took to study IgE receptor signaling. As a result of this work, we realized the value of a general-purpose software tool for rule-based modeling and work on BioNetGen began.

• B. Goldstein, J. R. Faeder, and W. S. Hlavacek (2004) Mathematical and computational models of immune-receptor signalling. Nat. Rev. Immunol., 4, 445-456. [10]

A review of mathematical modeling of signal transduction focused on immunoreceptors. Rule-based modeling is discussed.

• J. R. Faeder, M. L. Blinov, B. Goldstein, and W. S. Hlavacek (2005) Combinatorial complexity and dynamical restriction of network flows in signal transduction. Syst. Biol., 2, 5-15. [11]

Analyzes the distributions of species concentrations, reaction fluxes, and pathway utilization in the FcεRI model. Also demonstrates the infeasibility of model reduction by the ad hoc removal of species and reactions.

Growth Factor Receptors

• M. L. Blinov, J. R. Faeder, B. Goldstein, and W. S. Hlavacek (2006) A Network Model of Early Events in Epidermal Growth Factor Receptor Signaling That Accounts for Combinatorial Complexity. BioSystems, 83, 136-151. [12]

Presents a comparison between the Kholodenko et al. (1999) model of early events in EGFR signaling (PMID 10514507) and a rule-based expansion of the model developed using BioNetGen.

• D. Barua, J. R. Faeder, and J. M. Haugh (2007) Structure-based Kinetic Models of Modular Signaling Protein Function: Focus on Shp2. Biophys. J., 92, 2290-2300. [13]

Presents an application of BioNetGen2 to the regulation of Shp2 phosphatase activity, and the supplementary material provides an overview of the language and some advanced features, such as the include_molecules and exclude_molecules directives.

Miscellaneous

• M. L. Blinov, J. R. Faeder, J. Yang, B. Goldstein, and W. S. Hlavacek (2005) ‘On-the-fly’ or ‘generate-first’ modeling? Nat. Biotechnol., 23, 1344-1345. [14]

A discussion of the pros and cons of generating the network prior to or during a simulation as a comment on Lok and Brent, 2005 (PMID 15637632).

• M. L. Blinov, J. Yang, J. R. Faeder, and W. S. Hlavacek (2006) Depicting signaling cascades. Nat. Biotechnol., 24, 137-138. [15]

A suggestion for a rule-based extension of the process diagrams proposed by Kitano, et al. (PMID 16082367).

• Rubenstein R et al. (2007) Dynamics of the nucleated polymerization model of prion replication. Biophys. Chem. 125, 360-367. [16]

A paper about a model for self-aggregation of the prion protein. BioNetGen was used for model specificaton and simulation.

Detailed Balance

• D. Colquhoun, K. A. Dowsland, M. Beato, and A. J. R. Plested (2004). How to Impose Microscopic Reversibility in Complex Reaction Mechanisms. Biophys. J. 86, 3510-3518. pdf
• J. Yang, W. J. Bruno, W. S. Hlavacek, and J. Pearson. (2006). On imposing detailed balance in complex reaction mechanisms. Biophys. J. 91, 1136-1141. pdf
• M. Ederer and E. D. Gilles (2007). Thermodynamically Feasible Kinetic Models of Reaction Networks. Biophys. J. 92, 1846-1857.

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