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BioSimWare: A Software for the Modeling, Simulation and Analysis of Biological Systems

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Membrane Computing (CMC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6501))

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Abstract

BioSimWare is a novel software that provides a user-friendly framework for the modeling and stochastic simulation of complex biological systems, ranging from cellular processes to population phenomena. BioSimWare implements several stochastic algorithms to simulate the dynamics of single or multi-volume models, as well as automatic tools to analyze the effect of variation of the system parameters. BioSimWare supports SBML format, and can automatically convert stochastic models into the corresponding deterministic formulation. The main features of BioSimWare are presented in this paper, together with some applications which highlight the most relevant aspects of the computational tools that it provides.

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References

  1. Besozzi, D., Cazzaniga, P., Cocolo, S., Mauri, G., Pescini, D.: Modeling diffusion in a signal transduction pathway: the use of virtual volumes in P systems. To appear in International Journal of Foundations of Computer Science

    Google Scholar 

  2. Besozzi, D., Cazzaniga, P., Dugo, M., Pescini, D., Mauri, G.: A study on the combined interplay between stochastic fluctuations and the number of flagella in bacterial chemotaxis. EPTCS 6, 47–62 (2009)

    Article  Google Scholar 

  3. Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G.: Seasonal variance in P system models for metapopulations. Progress in Natural Science 17(4), 392–400 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  4. Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G.: A multivolume approach to stochastic modelling with membrane systems. In: Algorithmic Bioprocesses, pp. 519–542. Springer, Berlin (2009)

    Chapter  Google Scholar 

  5. Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G.: An analysis on the influence of network topologies on local and global dynamics of metapopulation systems. EPTCS 33, 1–17 (2010)

    Article  Google Scholar 

  6. Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D., Vanneschi, L.: A comparison of genetic algorithms and particle swarm optimization for parameter estimation in stochastic biochemical systems. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2009. LNCS, vol. 5483, pp. 116–127. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  7. Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G.: Modelling metapopulations with stochastic membrane systems. BioSystems 91(3), 499–514 (2008)

    Article  Google Scholar 

  8. Blake, W.J., Kærn, M., Cantor, C.R., Collins, J.J.: Noise in eukaryotic gene expression. Nature 422, 633–637 (2003)

    Article  Google Scholar 

  9. Cao, Y., Gillespie, D.T., Petzold, L.R.: The slow-scale stochastic simulation algorithm. Journal of Chemical Physics 122(1), 14116 (2005)

    Article  Google Scholar 

  10. Cao, Y., Gillespie, D.T., Petzold, L.R.: Efficient step size selection for the tau-leaping simulation method. Journal of Chemical Physics 124, 44109 (2006)

    Article  Google Scholar 

  11. Cao, Y., Gillespie, D.T., Petzold, L.R.: The adaptive explicit-implicit tau-leaping method with automatic tau selection. Journal of Chemical Physics 126, 224101 (2007)

    Article  Google Scholar 

  12. Cazzaniga, P.: Stochastic algorithms for biochemical processes. Ph.D. thesis, Università degli Studi di Milano-Bicocca (2010)

    Google Scholar 

  13. Cazzaniga, P., Mauri, G., Milanesi, L., Mosca, E., Pescini, D.: A novel variant of tissue P systems for the modelling of biochemical systems. In: Păun, G., Pérez-Jiménez, M.J., Riscos-Núñez, A., Rozenberg, G., Salomaa, A. (eds.) WMC 2009. LNCS, vol. 5957, pp. 210–226. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  14. Cazzaniga, P., Pescini, D., Besozzi, D., Mauri, G.: Tau leaping stochastic simulation method in P systems. In: Hoogeboom, H.J., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2006. LNCS, vol. 4361, pp. 298–313. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  15. Cazzaniga, P., Pescini, D., Besozzi, D., Mauri, G., Colombo, S., Martegani, E.: Modeling and stochastic simulation of the Ras/cAMP/PKA pathway in the yeast Saccharomyces cerevisiae evidences a key regulatory function for intracellular guanine nucleotides pools. Journal of Biotechnology 133(3), 377–385 (2008)

    Article  Google Scholar 

  16. Chaouiya, C.: Petri net modelling of biological networks. Briefings in Bioinformatics 8(4), 210–219 (2007)

    Article  Google Scholar 

  17. Craciun, G., Tang, Y., Feinberg, M.: Understanding bistability in complex enzyme-driven reaction networks. Proceedings of the National Academy of Sciences 103(23), 8697–8702 (2006)

    Article  MATH  Google Scholar 

  18. Cyto-Sim, http://www.cosbi.eu/index.php/research/prototypes/cyto-sim

  19. Elf, J., Ehrenberg, M.: Spontaneous separation of bi-stable biochemical systems into spatial domains of opposite phases. IEE Proceedings Systems Biology 1(2), 230–236 (2004)

    Article  Google Scholar 

  20. Elowitz, M.B., Levine, A.J., Siggia, E.D., Swain, P.S.: Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002)

    Article  Google Scholar 

  21. Gillespie, D.T.: General method for numerically simulating stochastic time evolution of coupled chemical-reactions. Journal of Computational Physics 22, 403–434 (1976)

    Article  MathSciNet  Google Scholar 

  22. Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry 81(25), 2340–2361 (1977)

    Article  Google Scholar 

  23. Gillespie, D.T.: Approximate accelerated stochastic simulation of chemically reacting systems. Journal of Chemical Physics 115(4), 1716–1733 (2001)

    Article  Google Scholar 

  24. Gillespie, D.T.: Stochastic simulation of chemical kinetics. Annual Review of Physical Chemistry 58, 35–55 (2007)

    Article  Google Scholar 

  25. Gillespie, D.T.: Simulation methods in systems biology. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 125–167. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  26. The GSL Web Page, http://www.gnu.org/software/gsl/

  27. Gunawan, R., Cao, Y., Petzold, L., Doyle, F.J.: Sensitivity analysis of discrete stochastic systems. Biophysical Journal 88, 2530–2540 (2005)

    Article  Google Scholar 

  28. Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  29. Hucka, M., et al.: The Systems Biology Markup Language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4), 524–531 (2003)

    Article  Google Scholar 

  30. The Infobiotic Web Page, http://www.infobiotic.org/

  31. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of the IEEE International Conference on Neural Networks, Piscataway, NJ, vol. IV, pp. 1942–1948 (1995)

    Google Scholar 

  32. Klipp, E., Liebermeister, W., Wierling, C., Kowald, A., Lehrach, H., Herwig, R.: Systems Biology: A Textbook. Wiley, Chichester (2009)

    Google Scholar 

  33. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  34. Lemerle, C., Di Ventura, B., Serrano, L.: Space as the final frontier in stochastic simulations of biological systems. FEBS Letters 579(8), 1789–1794 (2005)

    Article  Google Scholar 

  35. Leporati, A., Besozzi, D., Cazzaniga, P., Pescini, D., Ferretti, C.: Computing with energy and chemical reactions. Natural Computing 9(2), 493–512 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  36. Lipkow, K., Andrews, S.S., Bray, D.: Simulated diffusion of phosphorylated CheY through the cytoplasm of Escherichia coli. Journal of Bacteriology 187(1), 45–53 (2005)

    Article  Google Scholar 

  37. Marquez-Lago, T.T., Burrage, K.: Binomial tau-leap spatial stochastic simulation algorithm for applications in chemical kinetics. Journal of Chemical Physics 127(10), 104101 (2007)

    Article  Google Scholar 

  38. Martín-Vide, C., Pazos, J., Păun, G., Rodríguez-Patón, A.: A new class of symbolic abstract neural nets: Tissue P systems. In: Ibarra, O.H., Zhang, L. (eds.) COCOON 2002. LNCS, vol. 2387, pp. 573–679. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  39. McAdams, H., Arkin, A.: Stochastic mechanisms in gene expression. Proceedings of the National Academy of Sciences 94(3), 814–819 (1997)

    Article  Google Scholar 

  40. Meng, T.C., Somani, S., Dhar, P.: Modeling and simulation of biological systems with stochasticity. In Silico Biology 4, 24 (2004)

    Google Scholar 

  41. Moles, C.G., Mendes, P., Banga, J.R.: Parameter estimation in biochemical pathways: A comparison of global optimization methods. Genome Research 13(11), 2467–2474 (2003)

    Article  Google Scholar 

  42. Mosca, E., Cazzaniga, P., Merelli, I., Pescini, D., Mauri, G., Milanesi, L.: Stochastic simulations on a grid framework for parameter sweep applications in biological models. In: Int. Workshop on High Performance Computational Systems Biology, HiBi 2009, vol. 0, pp. 33–42. IEEE Computer Society, Los Alamitos (2009)

    Chapter  Google Scholar 

  43. The MP Virtual Laboratory, http://mplab.scienze.univr.it/

  44. The MPI standard Web Page, http://www-unix.mcs.anl.gov/mpi/

  45. P system modelling framework, http://www.dcs.shef.ac.uk/~marian/PSimulatorWeb/~P_Systems_applications.htm

  46. The P Systems Web Page, http://ppage.psystems.eu/

  47. Păun, G.: Computing with membranes. Journal of Computer and System Sciences 61(1), 108–143 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  48. Păun, G., Rozenberg, G., Salomaa, A. (eds.): The Oxford Handbook of Membrane Computing. Oxford University Press, Oxford (2010)

    MATH  Google Scholar 

  49. Pescini, D., Besozzi, D., Mauri, G., Zandron, C.: Dynamical probabilistic P systems. International Journal of Foundations of Computer Science 17(1), 183–204 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  50. Plyasunov, S., Arkin, A.: Efficient stochastic sensitivity analysis of discrete event systems. Journal of Computational Physics 221, 724–738 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  51. Pomerening, J.R.: Uncovering mechanisms of bistability in biological systems. Current Opinion in Biotechnology 19(4), 381–388 (2008)

    Article  Google Scholar 

  52. Pouton, C.W., Wagstaff, K.M., Roth, D.M., Moseley, G.W., Jans, D.A.: Targeted delivery to the nucleus. Advanced Drug Delivery Reviews 59(8), 698–717 (2007)

    Article  Google Scholar 

  53. The PRISM Web Page, http://www.prismmodelchecker.org/

  54. Rathinam, M., Petzold, L.R., Cao, Y., Gillespie, D.T.: Stiffness in stochastic chemically reacting systems: The implicit tau-leaping method. Journal of Chemical Physics 119, 12784–12794 (2003)

    Article  Google Scholar 

  55. Regev, A., Silverman, W., Shapiro, E.: Representation and simulation of biochemical processes using the pi-calculus process algebra. In: Pacific Symposium of Biocomputing (PSB 2001), pp. 459–470 (2001)

    Google Scholar 

  56. Reinker, S., Altman, R.M., Timmer, J.: Parameter estimation in stochastic biochemical reactions. In: IEE Proceedings Systems Biology, vol. 153, pp. 168–178 (2006)

    Google Scholar 

  57. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S.: Global Sensitivity Analysis: The Primer. Wiley Interscience, Hoboken (2008)

    MATH  Google Scholar 

  58. Saltelli, A., Ratto, M., Tarantola, S., Campolongo, F.: Sensitivity analysis for chemical models. Chemical Reviews 105, 2811–2827 (2005)

    Article  MATH  Google Scholar 

  59. The SBML portal, http://www.sbml.org/

  60. Szallasi, Z., Stelling, J., Periwal, V.: Systems Modeling in Cellular Biology. The MIT Press, Cambridge (2006)

    Book  MATH  Google Scholar 

  61. Turner, T.E., Schnell, S., Burrage, K.: Stochastic approaches for modelling in vivo reactions. Computational Biology and Chemistry 28, 165–178 (2004)

    Article  MATH  Google Scholar 

  62. Tyson, J.J.: Some further studies of nonlinear oscillations in chemical systems. Journal of Chemical Physics 58, 3919–3930 (1973)

    Article  Google Scholar 

  63. Vellela, M., Qian, H.: Stochastic dynamics and non-equilibrium thermodynamics of a bistable chemical system: the Schlögl model revisited. Journal of the Royal Society Interface 6(39), 925–940 (2009)

    Article  Google Scholar 

  64. Wadhams, G.H., Armitage, J.P.: Making sense of it all: bacterial chemotaxis. Nature Reviews Molecular Cell Biology 5(12), 1024–1037 (2004)

    Article  Google Scholar 

  65. Widder, S., Macía, J., Solé, R.: Monomeric bistability and the role of autoloops in gene regulation. PloS One 4(4), e5399 (2009)

    Article  Google Scholar 

  66. Wilhelm, T.: The smallest chemical reaction system with bistability. BMC Systems Biology 3(1), 90 (2009)

    Article  Google Scholar 

  67. Wilkinson, D.J.: Stochastic Modelling for Systems Biology. Chapman & Hall, Boca Raton (2006)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica e Comunicazione, Università degli Studi di Milano, Via Comelico 39, 20135, Milano, Italy

    Daniela Besozzi

  2. Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126, Milano, Italy

    Paolo Cazzaniga, Giancarlo Mauri & Dario Pescini

Authors
  1. Daniela Besozzi
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  2. Paolo Cazzaniga
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  3. Giancarlo Mauri
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  4. Dario Pescini
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Sheffield, Regent Court, Portobello Street, S1 4DP, Sheffield, UK

    Marian Gheorghe

  2. Department of Bioinformatics, School of Biology and Pharmacy, Friedrich Schiller University, Ernst-Abbe-Platz 1–4, 07743, Jena, Germany

    Thomas Hinze

  3. Institute of Mathematics of the Romanian Academy, P.O. Box 1-764, 014700, Bucureşti, Romania

    Gheorghe Păun

  4. Leiden Center of Advanced Computer Science (LIACS), Universiteit Leiden, Niels Bohrweg 1, 2333, CA Leiden, The Netherlands

    Grzegorz Rozenberg

  5. Turku Centre for Computer Science (TUCS), Leminkäisenkatu 14, 20520, Turku, Finland

    Arto Salomaa

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Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D. (2010). BioSimWare: A Software for the Modeling, Simulation and Analysis of Biological Systems. In: Gheorghe, M., Hinze, T., Păun, G., Rozenberg, G., Salomaa, A. (eds) Membrane Computing. CMC 2010. Lecture Notes in Computer Science, vol 6501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18123-8_12

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  • DOI: https://doi.org/10.1007/978-3-642-18123-8_12

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