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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
Cardelli, L.: Membrane interactions. In: BioConcur 2003, Workshop on Concurrent Models in Molecular Biology (2003)
Cao, Y., Gillespie, D.T., Petzold, L.R.: Efficient step size selection for the tau-leaping simulation method. J. Chem. Phys. 124, 044109 (2006)
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)
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)
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)
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)
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)
Fisher, J., Piterman, N., Hubbard, J., Stern, M., Harel, D.: Computational insights into C. elegans vulval development. PNAS 102(5), 1951–1956 (2005)
Gardiner, C.W.: Handbook of Stochastic Methods: For Physics, Chemistry and the Natural Sciences (Springer Series in Synergetics). Springer, Heidelberg (1996)
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)
Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: elements of reusable object-oriented software. Addison-Wesley, Reading (1995)
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)
Gillespie, D.T.: Exact Stochastic Simulation of Coupled Chemical Reactions. The Journal of Physical Chemistry B 81(25), 2340–2361 (1977)
Gillespie, D.T.: Approximate accelerated stochastic simulation of chemically reacting systems. The Journal of Chemical Physics (2001)
Harel, D.: Statecharts: A Visual Formalism for Complex Systems. Science of Computer Programming 8(3), 231–274 (1987)
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)
Jirstrand, M., Schmidt, H.: Systems biology toolbox for matlab: A computational platform for research in systems biology. Bioinformatics (2005)
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)
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)
Himmelspach, J., Uhrmacher, A.M.: Plug’n simulate. In: Proceedings of the Spring Simulation Multiconference, pp. 137–143. IEEE Computer Society, Los Alamitos (2007)
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)
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)
Kholodenko, B.N.: Cell-signalling dynamics in time and space. Nature Reviews Molecular Cell Biology 7(3), 165–176 (2006)
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)
Korn, G.A., Wait, J.V.: Digital continuous-system simulation. Prentice-Hall, Englewood Cliffs (1978)
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)
Minsky, M.: Models, minds, machines. In: Proc. IFIP Congress, pp. 45–49 (1965)
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)
Murata, T.: Petri Nets: Properties, Analysis and Applications. Proceedings of the IEEE 77(4), 541–574 (1989)
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)
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
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)
Priami, C.: Stochastic π-calculus. The Computer Journal 38(6), 578–589 (1995)
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)
Röhl, M., Morgenstern, S.: Composing simulation models using interface definitions based on web service descriptions. In: WSC 2007, pp. 815–822 (2007)
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)
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)
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)
Tian, T., Burrage, K.: Binomial leap methods for simulating stochastic chemical kinetics. The Journal of Chemical Physics 121(10356), 10356–10364 (2004)
Takahashi, K., Kaizu, K., Hu, B., Tomita, M.: A multi-algorithm, multi-timescale method for cell simulation. Bioinformatics 20, 538–546 (2004)
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)
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)
Uhrmacher, A.M.: Dynamic structures in modeling and simulation - a reflective approach. ACM Transactions on Modeling and Simulation 11(2), 206–232 (2001)
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)
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)
Zeigler, B.P., Praehofer, H., Kim, T.G.: Theory of Modeling and Simulation. Academic Press, London (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Uhrmacher, A.M. et al. (2008). One Modelling Formalism & Simulator Is Not Enough! A Perspective for Computational Biology Based on James II . In: Fisher, J. (eds) Formal Methods in Systems Biology. FMSB 2008. Lecture Notes in Computer Science(), vol 5054. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68413-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-68413-8_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68410-7
Online ISBN: 978-3-540-68413-8
eBook Packages: Computer ScienceComputer Science (R0)