Advertisement

One Modelling Formalism & Simulator Is Not Enough! A Perspective for Computational Biology Based on James II

  • Adelinde M. Uhrmacher
  • Jan Himmelspach
  • Matthias Jeschke
  • Mathias John
  • Stefan Leye
  • Carsten Maus
  • Mathias Röhl
  • Roland Ewald
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5054)

Abstract

Diverse modelling formalisms are applied in Computational Biology. Some describe the biological system in a continuous manner, others focus on discrete-event systems, or on a combination of continuous and discrete descriptions. Similarly, there are many simulators that support different formalisms and execution types (e.g. sequential, parallel-distributed) of one and the same model. The latter is often done to increase efficiency, sometimes at the cost of accuracy and level of detail. James II has been developed to support different modelling formalisms and different simulators and their combinations. It is based on a plug-in concept which enables developers to integrate spatial and non-spatial modelling formalisms (e.g. stochastic π calculus, Beta binders, Devs, space- π), simulation algorithms (e.g. variants of Gillespie’s algorithms (including Tau Leaping and Next Subvolume Method),space- π simulator, parallel Beta binders simulator) and supporting technologies (e.g. partitioning algorithms, data collection mechanisms, data structures, random number generators) into an existing framework. This eases method development and result evaluation in applied modelling and simulation as well as in modelling and simulation research.

Keywords

Modelling Formalism Simulation Algorithm Micro Model Process Algebra Movement Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [BR06]
    Broderick, G., Rubin, E.: The realistic modeling of biological systems: A workshop synopsis. ComPlexUs Modeling in Systems Biology, Social Cognitive and Information Science 3(4), 217–230 (2006)Google Scholar
  2. [Car03]
    Cardelli, L.: Membrane interactions. In: BioConcur 2003, Workshop on Concurrent Models in Molecular Biology (2003)Google Scholar
  3. [CGP06]
    Cao, Y., Gillespie, D.T., Petzold, L.R.: Efficient step size selection for the tau-leaping simulation method. J. Chem. Phys. 124, 044109 (2006)CrossRefGoogle Scholar
  4. [CLP04]
    Cao, Y., Li, H., Petzold, L.: Efficient formulation of the stochastic simulation algorithm for chemically reacting systems. The Journal of Chemical Physics 121(9), 4059–4067 (2004)CrossRefGoogle Scholar
  5. [EE04]
    Elf, J., Ehrenberg, M.: Spontaneous separation of bi-stable biochemical systems into spatial domains of opposite phases. Syst. Biol (Stevenage) 1(2), 230–236 (2004)CrossRefGoogle Scholar
  6. [EHU06]
    Ewald, R., Himmelspach, J., Uhrmacher, A.M.: Embedding a non-fragmenting partitioning algorithm for hierarchical models into the partitioning layer of James II. In: WSC 2006: Proceedings of the 38th conference on Winter simulation (2006)Google Scholar
  7. [EHUar]
    Ewald, R., Himmelspach, J., Uhrmacher, A.M.: An algorithm selection approach for simulation systems. In: Proceedings of the 22nd ACM/IEEE/SCS Workshop on Principles of Advanced and Distributed Simulation (PADS 2008) (to appear, 2008)Google Scholar
  8. [EMRU07]
    Ewald, R., Maus, C., Rolfs, A., Uhrmacher, A.M.: Discrete event modelling and simulation in systems biology. Journal of Simulation 1(2), 81–96 (2007)CrossRefGoogle Scholar
  9. [FPH+05]
    Fisher, J., Piterman, N., Hubbard, J., Stern, M., Harel, D.: Computational insights into C. elegans vulval development. PNAS 102(5), 1951–1956 (2005)CrossRefGoogle Scholar
  10. [Gar96]
    Gardiner, C.W.: Handbook of Stochastic Methods: For Physics, Chemistry and the Natural Sciences (Springer Series in Synergetics). Springer, Heidelberg (1996)Google Scholar
  11. [GB00]
    Gibson, M.A., Bruck, J.: Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels. J. Chem. Physics 104, 1876–1889 (2000)Google Scholar
  12. [GHJV95]
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: elements of reusable object-oriented software. Addison-Wesley, Reading (1995)Google Scholar
  13. [GHP07]
    Guerriero, M.L., Heath, J.K., Priami, C.: An automated translation from a narrative language for biological modelling into process algebra. In: Calder, M., Gilmore, S. (eds.) CMSB 2007. LNCS (LNBI), vol. 4695, pp. 136–151. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. [Gil77]
    Gillespie, D.T.: Exact Stochastic Simulation of Coupled Chemical Reactions. The Journal of Physical Chemistry B 81(25), 2340–2361 (1977)CrossRefGoogle Scholar
  15. [Gil01]
    Gillespie, D.T.: Approximate accelerated stochastic simulation of chemically reacting systems. The Journal of Chemical Physics (2001)Google Scholar
  16. [Har87]
    Harel, D.: Statecharts: A Visual Formalism for Complex Systems. Science of Computer Programming 8(3), 231–274 (1987)zbMATHCrossRefMathSciNetGoogle Scholar
  17. [HLP+06]
    Himmelspach, J., Lecca, P., Prandi, D., Priami, C., Quaglia, P., Uhrmacher, A.M.: Developing an hierarchical simulator for beta-binders. In: 20th Workshop on Principles of Advanced and Distributed Simulation (PADS 2006), pp. 92–102. IEEE Computer Society, Los Alamitos (2006)CrossRefGoogle Scholar
  18. [HS05]
    Jirstrand, M., Schmidt, H.: Systems biology toolbox for matlab: A computational platform for research in systems biology. Bioinformatics (2005)Google Scholar
  19. [HU04]
    Himmelspach, J., Uhrmacher, A.M.: A component-based simulation layer for james. In: ACM Press (ed.): PADS 2004: Proceedings of the eighteenth workshop on Parallel and distributed simulation, pp. 115–122. IEEE Computer Society, Los Alamitos (2004)CrossRefGoogle Scholar
  20. [HU07a]
    Himmelspach, J., Uhrmacher, A.M.: The event queue problem and pdevs. In: Proceedings of the SpringSim 2007, DEVS Integrative M&S Symposium, pp. 257–264. SCS (2007)Google Scholar
  21. [HU07b]
    Himmelspach, J., Uhrmacher, A.M.: Plug’n simulate. In: Proceedings of the Spring Simulation Multiconference, pp. 137–143. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  22. [JEP+ar]
    Jeschke, M., Ewald, R., Park, A., Fujimoto, R., Uhrmacher, A.M.: Parallel and distributed spatial simulation of chemical reactions. In: Proceedings of the 22nd ACM/IEEE/SCS Workshop on Principles of Advanced and Distributed Simulation (PADS 2008) (to appear, 2008)Google Scholar
  23. [JEU08]
    John, M., Ewald, R., Uhrmacher, A.M.: A spatial extension to the pi calculus. In: Proc. of the 1st Workshop From Biology To Concurrency and back (FBTC 2007). Electronic Notes in Theoretical Computer Science, vol. 194, pp. 133–148 (2008)Google Scholar
  24. [Kho06]
    Kholodenko, B.N.: Cell-signalling dynamics in time and space. Nature Reviews Molecular Cell Biology 7(3), 165–176 (2006)CrossRefGoogle Scholar
  25. [KK98]
    Karypis, G., Kumar, V.: MeTis: A Software Package for Partitioning Unstructured Graphs, Partitioning Meshes, and Computing Fill-Reducing Orderings of Sparse Matrices (Version 4.0) (September 1998)Google Scholar
  26. [KW78]
    Korn, G.A., Wait, J.V.: Digital continuous-system simulation. Prentice-Hall, Englewood Cliffs (1978)Google Scholar
  27. [LPU07]
    Leye, S., Priami, C., Uhrmacher, A.M.: A parallel beta-binders simulator. Technical Report 17/2007, The Microsoft Research - University of Trento Centre for Computational and Systems Biology (2007)Google Scholar
  28. [Min65]
    Minsky, M.: Models, minds, machines. In: Proc. IFIP Congress, pp. 45–49 (1965)Google Scholar
  29. [MJU07]
    Maus, C., John, M., Uhrmacher, A.M.: A multi-level and multi-formalism approach for model composition in systems biology. In: Conference on Computational Methods in Systems Biology, Edinburgh, Poster (2007)Google Scholar
  30. [Mur89]
    Murata, T.: Petri Nets: Properties, Analysis and Applications. Proceedings of the IEEE 77(4), 541–574 (1989)CrossRefGoogle Scholar
  31. [NS92]
    Nicollin, X., Sifakis, J.: An Overview and Synthesis on Timed Process Algebras. In: Larsen, K.G., Skou, A. (eds.) CAV 1991. LNCS, vol. 575, pp. 376–398. Springer, Heidelberg (1992)Google Scholar
  32. [OMG05]
    OMG. UML superstructure specification version 2.0 (document formal/05-07-04) (July 2005), http://www.omg.org/cgi-bin/doc?formal/05-07-04
  33. [PQ05]
    Priami, C., Quaglia, P.: Beta binders for biological interactions. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 20–33. Springer, Heidelberg (2005)Google Scholar
  34. [Pri95]
    Priami, C.: Stochastic π-calculus. The Computer Journal 38(6), 578–589 (1995)CrossRefGoogle Scholar
  35. [PRSS01]
    Priami, C., Regev, A., Shapiro, E., Silvermann, W.: Application of a stochastic name-passing calculus to representation and simulation of molecular processes. Information Processing Letters 80, 25–31 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  36. [RM07]
    Röhl, M., Morgenstern, S.: Composing simulation models using interface definitions based on web service descriptions. In: WSC 2007, pp. 815–822 (2007)Google Scholar
  37. [ROB05]
    Ramsey, S., Orrell, D., Bolouri, H.: Dizzy: Stochastic simulation of large scale genetic regulatory networks. Journal of Bioinformatics and Computational Biology 01(13), 415–436 (2005)CrossRefGoogle Scholar
  38. [RPS+04]
    Regev, A., Panina, E.M., Silverman, W., Cardelli, L., Shapiro, E.: BioAmbients: an abstraction for biological compartments. Theor. Comput. Sci. 325(1), 141–167 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  39. [RU06]
    Röhl, M., Uhrmacher, A.M.: Composing simulations from xml-specified model components. In: Proceedings of the Winter Simulation Conference 2006, pp. 1083–1090. ACM, New York (2006)CrossRefGoogle Scholar
  40. [TB04]
    Tian, T., Burrage, K.: Binomial leap methods for simulating stochastic chemical kinetics. The Journal of Chemical Physics 121(10356), 10356–10364 (2004)CrossRefGoogle Scholar
  41. [TKHT04]
    Takahashi, K., Kaizu, K., Hu, B., Tomita, M.: A multi-algorithm, multi-timescale method for cell simulation. Bioinformatics 20, 538–546 (2004)CrossRefGoogle Scholar
  42. [TNAT05]
    Takahashi, K., Nanda, S., Arjunan, V., Tomita, M.: Space in systems biology of signaling pathways: towards intracellular molecular crowding in silico. FEBS letters 579(8), 1783–1788 (2005)CrossRefGoogle Scholar
  43. [UEJ+07]
    Uhrmacher, A.M., Ewald, R., John, M., Maus, C., Jeschke, M., Biermann, S.: Combining micro and macro-modeling in devs for computational biology. In: Proc. of the 2007 Winter Simulation Conference, pp. 871–880 (2007)Google Scholar
  44. [Uhr01]
    Uhrmacher, A.M.: Dynamic structures in modeling and simulation - a reflective approach. ACM Transactions on Modeling and Simulation 11(2), 206–232 (2001)CrossRefGoogle Scholar
  45. [UHRE06]
    Uhrmacher, A.M., Himmelspach, J., Röhl, M., Ewald, R.: Introducing variable ports and multi-couplings for cell biological modeling in devs. In: Proc. of the 2006 Winter Simulation Conference, pp. 832–840 (2006)Google Scholar
  46. [vGB90]
    van Gunsteren, W.F., Berendsen, H.J.: Computer simulation of molecular dynamics: Methodology, applications, and perspectives in chemistry. Angewandte Chemie International Edition in English 29(9), 992–1023 (1990)CrossRefGoogle Scholar
  47. [ZPK00]
    Zeigler, B.P., Praehofer, H., Kim, T.G.: Theory of Modeling and Simulation. Academic Press, London (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Adelinde M. Uhrmacher
    • 1
  • Jan Himmelspach
    • 1
  • Matthias Jeschke
    • 1
  • Mathias John
    • 1
  • Stefan Leye
    • 1
  • Carsten Maus
    • 1
  • Mathias Röhl
    • 1
  • Roland Ewald
    • 1
  1. 1.University of RostockRostockGermany

Personalised recommendations