Abstract
The iterative process of adding details to a model while preserving its numerical behavior is called quantitative model refinement, and it has been previously discussed for ODE-based models and for kappa-based models. In this paper, we investigate and compare this approach in three different modeling frameworks: rule-based modeling, Petri nets and guarded command languages. As case study we use a model for the eukaryotic heat shock response that we refine to include the acetylation of the heat shock factor. We discuss how to perform the refinement in each of these frameworks in order to avoid the combinatorial state explosion of the refined model. We conclude that Bionetgen (and rule-based modeling in general) is well-suited for a compact representation of the refined model, Petri nets offer a good solution through the use of colors, while the PRISM refined model may be much larger than the basic model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Back, R., Ishdorj, T., Petre, I.: A petri net formalization of heat shock response model. In: Petre, I., Rozenberg, G. (eds.) Workshop on Natural Computing and Graph Transformations, pp. 19–28 (2008)
Blinov, M.L., Yang, J., Faeder, J.R., Hlavacek, W.S.: Graph theory for of biochemical networks. In: Priami, C., Ingólfsdóttir, A., Mishra, B., Riis Nielson, H. (eds.) Transactions on Computational Systems Biology VII. LNB, vol. 4230, pp. 89–106. Springer, Heidelberg (2006)
Czeizler, E., Rogojin, V., Petre, I.: The phosphorylation of the heat shock factor as a modulator for the heat shock response. In: Proceedings of the 9th International Conference on Computational Methods in Systems Biology, pp. 9–23. ACM (2011)
Danos, V., Feret, J., Fontana, W., Harmer, R., Krivine, J.: Rule-based modelling and model perturbation. In: Priami, C., Back, R.-J., Petre, I. (eds.) Transactions on Computational Systems Biology XI. LNCS, vol. 5750, pp. 116–137. Springer, Heidelberg (2009)
Faeder, J., Blinov, M., Goldstein, B., Hlavacek, W.: Rule-based modeling of biochemical networks. Complexity 10(4), 22–41 (2005)
Gratie, D., Iancu, B., Azimi, S., Petre, I.: Quantitative model refinement in four different frameworks, with applications to the heat shock response. Tech. Rep. 1067, TUCS (2013)
Harmer, R.: Rule-based modelling and tunable resolution. EPTCS 9, 65–72 (2009)
Heath, J.K., Kwiatkowska, M., Norman, G., Parker, D., Tymchyshyn, O.: Probabilistic model checking of complex biological pathways. In: Priami, C. (ed.) CMSB 2006. LNB, vol. 4210, pp. 32–47. Springer, Heidelberg (2006)
Hinton, A., Kwiatkowska, M., Norman, G., Parker, D.: Prism: A tool for automatic verification of probabilistic systems. In: Hermanns, H., Palsberg, J. (eds.) TACAS 2006. LNCS, vol. 3920, pp. 441–444. Springer, Heidelberg (2006)
Iancu, B., Czeizler, E., Czeizler, E., Petre, I.: Quantitative refinement of reaction models. IJUC 8(5-6), 529–550 (2012)
Iancu, B., Gratie, D., Azimi, S., Petre, I.: Computational modeling of the eukaryotic heat shock response: The bionetgen implementation, the petri net implementation and the prism implementation (2013), http://combio.abo.fi/research/computational-modeling-of-the-eukaryotic-heat-shock-response/
Kitano, H.: Systems biology: a brief overview. Science 295(5560), 1662–1664 (2002)
Kline, M., Morimoto, R.: Repression of the heat shock factor 1 transcriptional activation domain is modulated by constitutive phosphorylation. Molecular and cellular biology 17(4), 2107–2115 (1997)
Klipp, E., Herwig, R., Kowald, A., Wierling, C., Lehrach, H.: Systems biology in practice: Concepts, implementation and application. Wiley-Vch (2005)
Kwiatkowska, M., Norman, G., Parker, D.: Quantitative analysis with the probabilistic model checker prism. Electronic Notes in Theoretical Computer Science 153(2), 5–31 (2006)
Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: Verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)
Liu, F., Heiner, M., Yang, M.: An efficient method for unfolding colored petri nets. In: Laroque, C., Himmelspach, J., Pasupathy, R., Rose, O., Uhrmacher, A. (eds.) Proceedings of the Winter Simulation Conference, vol. 295. Winter Simulation Conference (2012)
Mizera, A., Czeizler, E., Petre, I.: Self-assembly models of variable resolution. In: Priami, C., Petre, I., de Vink, E. (eds.) Transactions on Computational Systems Biology XIV. LNCS, vol. 7625, pp. 181–203. Springer, Heidelberg (2012)
Murphy, E., Danos, V., Feret, J., Krivine, J., Harmer, R.: Rule Based Modelling and Model Refinement. In: Elements of Computational Systems Biology, pp. 83–114. Wiley Book Series on Bioinformatics, John Wiley & Sons, Inc. (2010)
Petre, I., Mizera, A., Hyder, C., Meinander, A., Mikhailov, A., Morimoto, R., Sistonen, L., Eriksson, J., Back, R.: A simple mass-action model for the eukaryotic heat shock response and its mathematical validation. Natural Computing 10(1), 595–612 (2011)
Raman, K., Chandra, N.: Systems biology. Resonance 15(2), 131–153 (2010)
Rieger, T., Morimoto, R., Hatzimanikatis, V.: Mathematical modeling of the eukaryotic heat-shock response: Dynamics of the hsp70 promoter. Biophysical Journal 88(3), 1646–1658 (2005)
Shi, Y., Mosser, D., Morimoto, R.: Molecular chaperones as hsf1-specific transcriptional repressors. Genes & Development 12(5), 654–666 (1998)
Westerheide, S., Anckar, J., Stevens Jr., S., Sistonen, L., Morimoto, R.: Stress-inducible regulation of heat shock factor 1 by the deacetylase sirt1. Science Signalling 323(5917), 1063–1066 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Iancu, B., Gratie, DE., Azimi, S., Petre, I. (2014). On the Implementation of Quantitative Model Refinement. In: Dediu, AH., Martín-Vide, C., Truthe, B. (eds) Algorithms for Computational Biology. AlCoB 2014. Lecture Notes in Computer Science(), vol 8542. Springer, Cham. https://doi.org/10.1007/978-3-319-07953-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-07953-0_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07952-3
Online ISBN: 978-3-319-07953-0
eBook Packages: Computer ScienceComputer Science (R0)