### RULE-BASED MODELING IN BIOUML

DOI: 10.12704/vb/e21

#### Abstract

*Motivation and Aim*: The traditional approach to mathematical modeling of biological systems involves usage of nonlinear systems of ordinary differential equations (ODEs) with given initial conditions. Talking about the modeling, we emphasize the fact that we consider an abstraction and study the mathematical description of some qualitative and quantitative characteristics of biological processes. The level of detail is dependent on the problem and based on the knowledge of the researcher. On the one hand, many meaningful models consist of few non-linear equations. On the other hand, a detailed study of the biochemical networks leads to development of large-scale models consisting of hundreds of variables and, therefore, equations. Moreover, if we incorporate to the model site-specific details of protein-protein interactions, the number of protein modifications increases dramatically, and complexity of the model becomes combinatorial. For example, a protein comprising *n* amino acids can be potentially found in 2*n* distinct phosphorylation states.

The principles for creation of the «rule-based» models were implemented in several software resources including KaSim (http://dev.executableknowledge.org/) and BioNetGen (www.bionetgen.org). BioUML supports the BioNetGen language (BNGL) and a special graphical notation created on the basis of SBGN and use it to visualize the «rule-based» models.

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Hlavacek W.S. (2009) How to deal with large models? Molecular Systems Biology, 5:240.

Faeder J., Blinov M. and Hlavacek W. (2009) Rule-based modeling of biochemical systems with BioNetGen. Methods Mol. Biol. 500: 113-167.

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.

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.

Resat H., Ewald J. A., Dixon D.A., Wiley H. S. (2003) An integrated model of epidermal growth factor receptor trafficking and signal transduction. Biophys J. 85(2): 730-43.

Maly I.V., Wiley H.S., Lauffenburger D.A. (2004 Jan) Self-organization of polarized cell signaling via autocrine circuits: computational model analysis. Biophys J. 86 (1 Pt 1): 10-22.

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.

Blinov M.L., Moraru I.I. Leveraging Modeling Approaches: Reaction Networks and Rules. Adv Exp Med Biol. 2012 ; 736: 517–530.

Krambeck F.J., Betenbaugh M.J. A mathematical model of N-linked Biotechnology and Bioengineering. 2005. V. 92. № 6. P. 711–728.

Krambeck F.J., Bennun S.V., Narang S., Choi S., Yarema K.J., Betenbaugh M.J. A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data. Glycobiology. 2009. V. 19. № 11. P. 1163–1175.

Bennun S.V., Yarema K.J., Betenbaugh M.J., Krambeck F.J. Integration of the transcriptome and glycome for identification of glycan cell signatures. PLoS Computational Biology. 2013. V. 9. № 1. e1002813.

Le Novère N., Hucka M., Mi H., Moodie S., Schreiber F., Sorokin A., Demir E., Wegner K., Aladjem M.I., Wimalaratne S.M., et al. The Systems Biology Graphical Notation // Nature Biotechnology. 2009. V. 27, № 8. PP. 735-741.

Kanehisa M., Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res., 2000, 28(1): 27–30.

Kitano H, Funahashi A, Matsuoka Y, Oda K. (2005) Using process diagrams for the graphical representation of biological networks. Nat Biotechnol. 23(8): 961-6.

Banin E., Neuberger Y., Altshuler Y., Halevi A., Inbar O., Nir D., Dukler A. A novel linear code (R) nomenclature for complex carbohydrates. Trends in Glycoscience and Glycotechnology. 2002. V. 14. № 77. P. 127–137.

Brown P.N., Byrne G.D., Hindmarsh A.C. VODE: A Variable-Coefficient ODE Solver // SIAM Journal on Scientific and Statistical Computing. 1989. V. 10. PP. 1038-1051.

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: 3289-91.

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