Abstract
Understanding how genes regulate each other and how gene expression is controlled in living cells is crucial to cure genetic diseases such as cancer and represents a fundamental step towards personalised medicine. The complexity and the high concurrency of gene regulatory networks require the use of formal techniques to analyse the dynamical properties that control cell proliferation and differentiation. However, for these techniques to be used and be useful, they must be accessible to biologists, who are currently not trained to operate with abstract formal models of concurrency. Petri nets, owing to their appealing graphical representation, have proved to be able to bridge this interdisciplinary gap and provide an accessible framework for the construction and execution of biological networks. In this paper, we propose a novel Petri net representation, tightly designed around the classic basic definition of the formalism by introducing only a small number of extensions while making the framework intuitively accessible to a biology-trained audience with no expertise in concurrency theory. Finally, we show how this Petri net framework has been successfully applied in practice to capture haematopoietic stem cell differentiation, and the value of this approach in understanding the heterogeneity of a stem cell population.
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
World Health Organization: World Health Statistics 2012 (2012)
Koch, I., Junker, B.H., Heiner, M.: Application of petri net theory for modelling and validation of the sucrose breakdown pathway in the potato tuber. Bioinformatics 21(7), 1219–1226 (2005)
Gilbert, D., Heiner, M., Lehrack, S.: A unifying framework for modelling and analysing biochemical pathways using petri nets. In: Calder, M., Gilmore, S. (eds.) CMSB 2007. LNCS (LNBI), vol. 4695, pp. 200–216. Springer, Heidelberg (2007)
Bonzanni, N., Krepska, E., Feenstra, K.A., Fokkink, W., Kielmann, T., Bal, H., Heringa, J.: Executing multicellular differentiation: quantitative predictive modelling of c. elegans vulval development. Bioinformatics 25(16), 2049–2056 (2009)
Bonzanni, N., Zhang, N., Oliver, S.G., Fisher, J.: The role of proteosome-mediated proteolysis in modulating potentially harmful transcription factor activity in Saccharomyces cerevisiae. Bioinformatics 27(13), I283–I287 (2011)
Steggles, L.J., Banks, R., Shaw, O., Wipat, A.: Qualitatively modelling and analysing genetic regulatory networks: a petri net approach. Bioinformatics 23(3), 336–343 (2007)
Chaouiya, C., Remy, É., Ruet, P., Thieffry, D.: Qualitative modelling of genetic networks: From logical regulatory graphs to standard petri nets. In: Cortadella, J., Reisig, W. (eds.) ICATPN 2004. LNCS, vol. 3099, pp. 137–156. Springer, Heidelberg (2004)
Grunwald, S., Speer, A., Ackermann, J., Koch, I.: Petri net modelling of gene regulation of the duchenne muscular dystrophy. Biosystems 92(2), 189–205 (2008)
Matsuno, H., Doi, A., Nagasaki, M., Miyano, S.: Hybrid petri net representation of gene regulatory network. In: Pacific Symposium on Biocomputing, vol. 5, pp. 338–349 (2000)
Bonzanni, N., Garg, A., Feenstra, K.A., Schütte, J., Kinston, S., Miranda-Saavedra, D., Heringa, J., Xenarios, I., Göttgens, B.: Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model. Bioinformatics 29(13), i80–i88 (2013)
Tarjan, R.: Depth-first search and linear graph algorithms. SIAM Journal on Computing 1(2), 146–160 (1975)
Krepska, E.: Towards Big Biology: High-Performance Verification of Large Concurrent Systems. PhD thesis, VU University Amsterdam (2012)
Garg, A., Xenarios, I., Mendoza, L., DeMicheli, G.: An efficient method for dynamic analysis of gene regulatory networks and in silico gene perturbation experiments. In: Speed, T., Huang, H. (eds.) RECOMB 2007. LNCS (LNBI), vol. 4453, pp. 62–76. Springer, Heidelberg (2007)
Grass, J.A., Boyer, M.E., Pal, S., Wu, J., Weiss, M.J., Bresnick, E.H.: Gata-1-dependent transcriptional repression of gata-2 via disruption of positive autoregulation and domain-wide chromatin remodeling. Proceedings of the National Academy of Sciences 100(15), 8811–8816 (2003)
Doi, A., Nagasaki, M., Matsuno, H., Miyano, S.: Simulation-based validation of the p53 transcriptional activity with hybrid functional petri net. Silico Biology 6(1), 1–13 (2006)
Will, J., Heiner, M.: Petri nets in biology, chemistry, and medicine - bibliography. Technical Report 04/2002, BTU Cottbus, Computer Science (2002)
Chaouiya, C.: Petri net modelling of biological networks. Briefings in Bioinformatics 8(4), 210 (2007)
Heiner, M., Gilbert, D., Donaldson, R.: Petri nets for systems and synthetic biology. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 215–264. Springer, Heidelberg (2008)
Kauffman, S., Peterson, C., Samuelsson, B., Troein, C.: Random boolean network models and the yeast transcriptional network. Proceedings of the National Academy of Sciences of the United States of America 100(25), 14796–14799 (2003)
Thomas, R.: Regulatory networks seen as asynchronous automata: A logical description. Journal of Theoretical Biology 153(1), 1–23 (1991)
Heiner, M., Herajy, M., Liu, F., Rohr, C., Schwarick, M.: Snoopy – A unifying petri net tool. In: Haddad, S., Pomello, L. (eds.) PETRI NETS 2012. LNCS, vol. 7347, pp. 398–407. Springer, Heidelberg (2012)
Gonzalez, A.G., Naldi, A., Sánchez, L., Thieffry, D., Chaouiya, C.: Ginsim: A software suite for the qualitative modelling, simulation and analysis of regulatory networks. Biosystems 84(2), 91–100 (2006)
Nagasaki, M., Saito, A., Jeong, E., Li, C., Kojima, K., Ikeda, E., Miyano, S.: Cell illustrator 4.0: A computational platform for systems biology. Silico Biology 10(1-2), 5–26 (2010)
Marwan, W., Rohr, C., Heiner, M.: Petri nets in snoopy: A unifying framework for the graphical display, computational modelling, and simulation of bacterial regulatory networks. In: Helden, J., Toussaint, A., Thieffry, D. (eds.) Bacterial Molecular Networks. Methods in Molecular Biology, vol. 804, pp. 409–437. Springer (2012)
Doi, A., Nagasaki, M., Matsuno, H., Miyano, S.: Simulation-based validation of the p53 transcriptional activity with hybrid functional petri net. Silico Biology 6(1), 1–13 (2006)
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
Bonzanni, N., Feenstra, K.A., Fokkink, W., Heringa, J. (2014). Petri Nets Are a Biologist’s Best Friend. In: Fages, F., Piazza, C. (eds) Formal Methods in Macro-Biology. FMMB 2014. Lecture Notes in Computer Science(), vol 8738. Springer, Cham. https://doi.org/10.1007/978-3-319-10398-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-10398-3_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10397-6
Online ISBN: 978-3-319-10398-3
eBook Packages: Computer ScienceComputer Science (R0)