Translation from the Quantified Implicit Process Flow Abstraction in SBGN-PD Diagrams to Bio-PEPA Illustrated on the Cholesterol Pathway

  • Laurence Loewe
  • Maria Luisa Guerriero
  • Steven Watterson
  • Stuart Moodie
  • Peter Ghazal
  • Jane Hillston

Abstract

For a long time biologists have used visual representations of biochemical networks to gain a quick overview of important structural properties. Recently SBGN, the Systems Biology Graphical Notation, has been developed to standardise the way in which such graphical maps are drawn in order to facilitate the exchange of information. Its qualitative Process Description (SBGN-PD) diagrams are based on an implicit Process Flow Abstraction (PFA) that can also be used to construct quantitative representations, which facilitate automated analyses of the system. Here we explicitly describe the PFA that underpins SBGN-PD and define attributes for SBGN-PD glyphs that make it possible to capture the quantitative details of a biochemical reaction network. Such quantitative details can be used to automatically generate an executable model. To facilitate this, we developed a textual representation for SBGN-PD called “SBGNtext” and implemented SBGNtext2BioPEPA, a tool that demonstrates how Bio-PEPA models can be generated automatically from SBGNtext. Bio-PEPA is a process algebra that was designed for implementing quantitative models of concurrent biochemical reaction systems. The scheme developed here is general and can be easily adapted to other output formalisms. To illustrate the intended workflow, we model the metabolic pathway of the cholesterol synthesis. We use this to compute the statin dosage response of the flux through the cholesterol pathway for different concentrations of the enzyme HMGCR that is inhibited by statin.

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References

  1. 1.
    Akman, O.E., Guerriero, M.L., Loewe, L., Troein, C.: Complementary approaches to understanding the plant circadian clock. In: Proc. of FBTC 2010. EPTCS, vol. 19, pp. 1–19 (2010)Google Scholar
  2. 2.
    Baigent, C., Keech, A., Kearney, P.M., Blackwell, L., Buck, G., Pollicino, C., Kirby, A., Sourjina, T., Peto, R., Collins, R., Simes, R.: Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 366, 1267–1278 (2005)CrossRefGoogle Scholar
  3. 3.
    Bio-PEPA homepage, http://www.biopepa.org/; To install the Bio-PEPA Eclipse Plug-in by Adam Duguid follow the links from http://homepages.inf.ed.ac.uk/jeh/Bio-PEPA/Tools.html (2009)
  4. 4.
    Calder, M., Duguid, A., Gilmore, S., Hillston, J.: Stronger computational modelling of signalling pathways using both continuous and discrete-state methods. In: Priami, C. (ed.) CMSB 2006. LNCS (LNBI), vol. 4210, pp. 63–77. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Calder, M., Hillston, J.: Process algebra modelling styles for biomolecular processes. In: Priami, C., Back, R.-J., Petre, I. (eds.) Transactions on Computational Systems Biology XI. LNCS (LNBI), vol. 5750, pp. 1–25. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Chandran, D., Bergmann, F., Sauro, H.: TinkerCell: modular CAD tool for synthetic biology. Journal of Biological Engineering 3(1), 19 (2009), http://www.tinkercell.com CrossRefGoogle Scholar
  7. 7.
    Chang, A., Scheer, M., Grote, A., Schomburg, I., Schomburg, D.: BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009. Nucleic Acids Res. 37, D588–D592 (2009), http://www.brenda-enzymes.org/ CrossRefGoogle Scholar
  8. 8.
    Chasman, D.I., Posada, D., Subrahmanyan, L., Cook, N.R., Stanton Jr., V.P., Ridker, P.M.: Pharmacogenetic study of statin therapy and cholesterol reduction. JAMA 291, 2821–2827 (2004)CrossRefGoogle Scholar
  9. 9.
    Ciocchetta, F., Gilmore, S., Guerriero, M.L., Hillston, J.: Integrated Simulation and Model-Checking for the Analysis of Biochemical Systems. In: Proc. of PASM 2008. ENTCS, vol. 232, pp. 17–38 (2009)Google Scholar
  10. 10.
    Ciocchetta, F., Hillston, J.: Bio-PEPA: a Framework for the Modelling and Analysis of Biological Systems. Theoretical Computer Science 410(33-34), 3065–3084 (2009)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Cytoscape Consortium: Cytoscape Home page (2009), http://cytoscape.org/
  12. 12.
    Danos, V., Feret, J., Fontana, W., Harmer, R., Krivine, J.: Rule-based modelling of cellular signalling. In: Caires, L., Li, L. (eds.) CONCUR 2007. LNCS, vol. 4703, pp. 17–41. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Danos, V., Laneve, C.: Formal molecular biology. Theoretical Computer Science 325, 69–110 (2004)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Dematté, L., Priami, C., Romanel, A.: The BlenX Language: A Tutorial. In: Bernardo, M., Degano, P., Tennenholtz, M. (eds.) SFM 2008. LNCS, vol. 5016, pp. 313–365. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Demir, E., Babur, O., Dogrusoz, U., Gursoy, A., Nisanci, G., Cetin-Atalay, R., Ozturk, M.: PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways. Bioinformatics 18, 996–1003 (2002)CrossRefGoogle Scholar
  16. 16.
    Draerger, A., Hassis, N., Supper, J., Schröder, A.Z.: SBMLsqueezer: A CellDesigner plug-in to generate kinetic rate equations for biochemical networks. BMC Systems Biology 2, 39 (2008)CrossRefGoogle Scholar
  17. 17.
    Duguid, A., Gilmore, S., Guerriero, M.L., Hillston, J., Loewe, L.: Design and Development of Software Tools for Bio-PEPA. In: Proc. of WSC 2009, pp. 956–967. IEEE Press, Los Alamitos (2009)Google Scholar
  18. 18.
    Funahashi, A., Matsuoka, Y., Jouraku, A., Morohashi, M., Kikuchi, N., Kitano, H.: CellDesigner 3.5: A Versatile Modeling Tool for Biochemical Networks. Proceedings of the IEEE 96(issue 8), 1254–1265 (2008), http://www.celldesigner.org/ CrossRefGoogle Scholar
  19. 19.
    Gibson, M.A., Bruck, J.: Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels. J. Phys. Chem. 104, 1876–1889 (2000)CrossRefGoogle Scholar
  20. 20.
    Gillespie, D.T.: Stochastic Simulation of Chemical Kinetics. Annu. Rev. Phys. Chem. 58, 35–55 (2007)CrossRefGoogle Scholar
  21. 21.
    Goldstein, J.L., Brown, M.S.: Regulation of the mevalonate pathway. Nature 343, 425–430 (1990)CrossRefGoogle Scholar
  22. 22.
    Heiner, M., Richter, R., Schwarick, M., Rohr, C.: Snoopy – A tool to design and execute graph-based formalisms. Petri Net Newsletter 74, 8–22 (2008), http://www-dssz.informatik.tu-cottbus.de/software/snoopy.html Google Scholar
  23. 23.
    Hillston, J.: A Compositional Approach to Performance Modelling. Cambridge University Press, Cambridge (1996)CrossRefMATHGoogle Scholar
  24. 24.
    Hlavacek, W.S., Faeder, J.R., Blinov, M.L., Posner, R.G., Hucka, M., Fontana, W.: Rules for modeling signal-transduction systems. Science STKE 344, re6 (2006)Google Scholar
  25. 25.
    Hucka, M., Hoops, S., Keating, S., Le Novère, N., Sahle, S., Wilkinson, D.: Systems Biology Markup Language (SBML) Level 2 Version 4 Release 1. Nature Proceedings (2008), http://dx.doi.org/10.1038/npre.2008.2715.1 and http://sbml.org/Documents/Specifications
  26. 26.
    Jansson, A., Jirstrand, M.: Biochemical modeling with Systems Biology Graphical Notation. Drug Discovery Today (2010)Google Scholar
  27. 27.
    Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., Hirakawa, M.: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38, D355–D360 (2010), http://www.genome.jp/kegg/ CrossRefGoogle Scholar
  28. 28.
    Kitano, H., Funahashi, A., Matsuoka, Y., Oda, K.: Using process diagrams for the graphical representation of biological networks. Nature Biotechnology 23, 961–966 (2005)CrossRefGoogle Scholar
  29. 29.
    Kohn, K.W., Aladjem, M.I., Kim, S., Weinstein, J.N., Pommier, Y.: Depicting combinatorial complexity with the molecular interaction map notation. Mol. Syst. Biol. 2, 51 (2006)Google Scholar
  30. 30.
    Krauss, R.M., Mangravite, L.M., Smith, J.D., Medina, M.W., Wang, D., Guo, X., Rieder, M.J., Simon, J.A., Hulley, S.B., Waters, D., Saad, M., Williams, P.T., Taylor, K.D., Yang, H., Nickerson, D.A., Rotter, J.I.: Variation in the 3-hydroxyl-3-methylglutaryl coenzyme a reductase gene is associated with racial differences in low-density lipoprotein cholesterol response to simvastatin treatment. Circulation 117, 1537–1544 (2008)CrossRefGoogle Scholar
  31. 31.
    Law, M.R., Wald, N.J., Rudnicka, A.R.: Quantifying effect of statins on low density lipoprotein cholesterol, ischaemic heart disease, and stroke: systematic review and meta-analysis. BMJ 326, 1423 (2003)CrossRefGoogle Scholar
  32. 32.
    Le Novère, N., Hucka, M., Mi, H., Moodie, S., Schreiber, F., Sorokin, A., Demir, E., Wegner, K., Aladjem, M.I., Wimalaratne, S.M., Bergman, F.T., Gauges, R., Ghazal, P., Kawaji, H., Li, L., Matsuoka, Y., Villeger, A., Boyd, S.E., Calzone, L., Courtot, M., Dogrusoz, U., Freeman, T.C., Funahashi, A., Ghosh, S., Jouraku, A., Kim, S., Kolpakov, F., Luna, A., Sahle, S., Schmidt, E., Watterson, S., Wu, G., Goryanin, I., Kell, D.B., Sander, C., Sauro, H., Snoep, J.L., Kohn, K., Kitano, H.: The Systems Biology Graphical Notation. Nature Biotechnology 27, 735–741 (2009)CrossRefGoogle Scholar
  33. 33.
    Le Novère, N., Moodie, S., Sorokin, A., Hucka, M., Schreiber, F., Demir, E., Mi, H., Matsuoka, Y., Wegner, K., Kitano, H.: Systems Biology Graphical Notation: Process Diagram Level 1. Nature Preceedings (2008), http://hdl.handle.net/10101/npre.2008.2320.1
  34. 34.
    Loewe, L.: The SBGNtext2BioPEPA homepage (2009), http://csbe.bio.ed.ac.uk/SBGNtext2BioPEPA/index.php
  35. 35.
    Loewe, L., Moodie, S., Hillston, J.: Defining a textual representation for SBGN Process Diagrams and translating it to Bio-PEPA for quantitative analysis of the MAPK signal transduction cascade. Tech. rep., School of Informatics, University of Edinburgh (2009), http://csbe.bio.ed.ac.uk/SBGNtext2BioPEPA/index.php
  36. 36.
    Loewe, L., Moodie, S., Hillston, J.: Quantifying the implicit process flow abstraction in SBGN-PD diagrams with Bio-PEPA. In: Proc. of CompMod 2009. EPTCS, vol. 6, pp. 93–107 (2009), http://arxiv.org/abs/0910.1410
  37. 37.
    Medina, M.W., Gao, F., Ruan, W., Rotter, J.I., Krauss, R.M.: Alternative splicing of 3-hydroxy-3-methylglutaryl coenzyme A reductase is associated with plasma low-density lipoprotein cholesterol response to simvastatin. Circulation 118, 355–362 (2008)CrossRefGoogle Scholar
  38. 38.
    Moodie, S.L., Sorokin, A., Goryanin, I., Ghazal, P.: A Graphical Notation to Describe the Logical Interactions of Biological Pathways. J. Integr. Bioinformatics 3(2), 36 (2006)Google Scholar
  39. 39.
    Parr, T.: The Definitive ANTLR Reference: Building Domain-Specific Languages. The Pragmatic Bookshelf, Raleigh (2007), http://www.antlr.org/ Google Scholar
  40. 40.
    Phillips, A.: A Visual Process Calculus for Biology. In: Symbolic Systems Biology: Theory and Methods, Jones and Bartlett Publishers (to appear, 2010), http://research.microsoft.com/en-us/projects/spim/
  41. 41.
    Priami, C.: Stochastic π-calculus. The Computer Journal 38(7), 578–589 (1995)CrossRefGoogle Scholar
  42. 42.
    Ramsey, S., Orrell, D., Bolouri, H.: Dizzy: stochastic simulation of large-scale genetic regulatory networks. J. Bioinf. Comp. Biol. 3(2), 415–436 (2005), http://magnet.systemsbiology.net/software/Dizzy/ CrossRefGoogle Scholar
  43. 43.
    Raza, S., Robertson, K.A., Lacaze, P.A., Page, D., Enright, A.J., Ghazal, P., Freeman, T.C.: A logic-based diagram of signalling pathways central to macrophage activation. BMC Syst. Biol. 2, 36 (2008)CrossRefGoogle Scholar
  44. 44.
    Sauro, H.M., Hucka, M., Finney, A., Wellock, C., Bolouri, H., Doyle, J., Kitano, H.: Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration. OMICS 7(4), 355–372 (2003), For the graphical front end ”JDesigner” http://www.sys-bio.org/software/jdesigner.htm CrossRefGoogle Scholar
  45. 45.
    Shukla, A.: Mapping the Edinburgh Pathway Notation to the Performance Evaluation Process Algebra. Master’s thesis, University of Trento, Italy (2007)Google Scholar
  46. 46.
    Sorokin, A., Paliy, K., Selkov, A., Demin, O., Dronov, S., Ghazal, P., Goryanin, I.: The Pathway Editor: A tool for managing complex biological networks. IBM J. Res. Dev. 50, 561–573 (2006), http://www.bioinformatics.ed.ac.uk/epe/; This work used the Edinburgh Pathway Editor prototype version EPE-3.0.0-alpha13 from http://epe.sourceforge.net/SourceForge/EPE.html CrossRefGoogle Scholar
  47. 47.
    Villéger, A.C., Pettifer, S.R., Kell, D.B.: Arcadia: a visualization tool for metabolic pathways. Bioinformatics 26(11), 1470–1471 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Laurence Loewe
    • 1
  • Maria Luisa Guerriero
    • 1
  • Steven Watterson
    • 1
    • 2
  • Stuart Moodie
    • 3
  • Peter Ghazal
    • 1
    • 2
  • Jane Hillston
    • 1
    • 3
  1. 1.Centre for System Biology at Edinburgh, King’s BuildingsThe University of EdinburghEdinburghScotland
  2. 2.Division of Pathway MedicineThe University of EdinburghUK
  3. 3.School of InformaticsThe University of EdinburghUK

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