Abstract
This work presents a platform for the modelling, simulation and automatic parametrization of semi-quantitative metabolic networks. Starting from a network modelled through Petri Nets (PN) and represented in SBML, the platform converts the model into an internal representation implemented through an Electronic Design Automation (EDA) description language. It applies techniques and tools well established in the EDA field to simulate the model and to automate the network parametrization. We present the validation of the model simulation and of the parameters automatically extrapolated by the platform with the state of art modelling and simulation tools for PNs. The validation uses a real metabolic network and shows the platform opportunities and limitations in reproducing the experimental results, simulating the models in different conditions, and facilitating the analysis of the dynamics that regulate the network.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The framework relies on the IBM FoCs synthesizer for the automatic synthesis of assertions.
References
Balazki, P., Lindauer, K., Einloft, J., Ackermann, J., Koch, I.: MONALISA for stochastic simulations of Petri Net models of biochemical systems. BMC Bioinform. 16(1), 215 (2015). https://doi.org/10.1186/s12859-015-0596-y
Barabasi, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet 5(2), 101 (2004)
Bombieri, N., et al.: Reusing RTL assertion checkers for verification of SystemC TLM models. J. Electron. Test. 31(2), 167–180 (2015). https://doi.org/10.1007/s10836-015-5514-8
Bombieri, N., Fummi, F., Pravadelli, G.: On the evaluation of transactor-based verification for reusing TLM assertions and testbenches at RTL. In: Proceedings of ACM/IEEE DATE, pp. 1–6 (2006)
Caligola, S., et al.: Efficient simulation and parametrization of stochastic Petri Nets in systemc: A case study from systems biology. In: 2019 Forum for Specification and Design Languages (FDL), pp. 1–7 (2019)
Chaouiya, C.: Petri Net modelling of biological networks. Brief. Bioinform. 8(4), 210–219 (2007)
Coelho, C.N., Foster, H.D.: Assertion-based verification. In: Drechsler, R. (ed.) Advanced Formal Verification, vol. 4. Springer, Boston (2004). https://doi.org/10.1007/1-4020-2530-0_5
Eleftheriadis, T., et al.: Uric acid induces caspase-1 activation, il-1\(\beta \) secretion and p2x7 receptor dependent proliferation in primary human lymphocytes. Hippokratia 17(2), 141 (2013)
Gillespie, D.T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comput. Phys. 22(4), 403–434 (1976)
Hoffmann, A., Levchenko, A., Scott, M.L., Baltimore, D.: The i\(\kappa \)b-nf-\(\kappa \)b signaling module: temporal control and selective gene activation. Science 298(5596), 1241–1245 (2002)
Hoops, S., et al.: COPASI - A COmplex PAthway SImulator. Bioinformatics 22(24), 3067–3074 (2006)
Jeong, H., Mason, S.P., Barabási, A.L., Oltvai, Z.N.: Lethality and centrality in protein networks. Nature 411(6833), 41 (2001)
Klamt, S., Saez-Rodriguez, J., Lindquist, J.A., Simeoni, L., Gilles, E.D.: A methodology for the structural and functional analysis of signaling and regulatory networks. BMC bioinformatics 7(1), 56 (2006). https://doi.org/10.1186/1471-2105-7-56
Koch, I.: Petri Nets in systems biology. Softw. Syst. Model. 14(2), 703–710 (2014). https://doi.org/10.1007/s10270-014-0421-5
Lane, A.N., Fan, T.W.M.: Regulation of mammalian nucleotide metabolism and biosynthesis. Nucleic Acids Res. 43(4), 2466–2485 (2015)
Morris, M.K., Saez-Rodriguez, J., Sorger, P.K., Lauffenburger, D.A.: Logic-based models for the analysis of cell signaling networks. Biochemistry 49(15), 3216–3224 (2010)
Piccio, L., et al.: Molecular mechanisms involved in lymphocyte recruitment in inflamed brain microvessels: critical roles for P-selectin glycoprotein ligand-1 and heterotrimeric Gi-linked receptors. J. Immunol. 168(4), 1940–1949 (2002)
Puchałka, J., Kierzek, A.M.: Bridging the gap between stochastic and deterministic regimes in the kinetic simulations of the biochemical reaction networks. Biophys. J. 86(3), 1357–1372 (2004)
Rohr, C., Marwan, W., Heiner, M.: Snoopy-a unifying Petri net framework to investigate biomolecular networks. Bioinformatics 26(7), 974–975 (2010)
Steuer, R., Junker, B.H.: Computational models of metabolism: stability and regulation in metabolic networks. Adv. Chem. Phys. 142, 105 (2009)
Thanh, V.H., Priami, C.: Simulation of biochemical reactions with time-dependent rates by the rejection-based algorithm. J. Chem. Phys. 143(5), 08B601\(\_\)1 (2015)
Acknowledgements
G.C. was supported by the European Research Council (ERC) grants IMMUNO ALZHEIMER (nr. 695714, ERC advanced grant).
R. G. is supported by GNCS-INDAM and JPND 2019-466-037.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bombieri, N. et al. (2020). On the Simulation and Automatic Parametrization of Metabolic Networks Through Electronic Design Automation. In: Cazzaniga, P., Besozzi, D., Merelli, I., Manzoni, L. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. Lecture Notes in Computer Science(), vol 12313. Springer, Cham. https://doi.org/10.1007/978-3-030-63061-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-63061-4_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63060-7
Online ISBN: 978-3-030-63061-4
eBook Packages: Computer ScienceComputer Science (R0)