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

  • Daniela Besozzi
  • Paolo Cazzaniga
  • Giancarlo Mauri
  • Dario Pescini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6501)

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. 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 ScienceGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)MathSciNetCrossRefMATHGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 7.
    Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G.: Modelling metapopulations with stochastic membrane systems. BioSystems 91(3), 499–514 (2008)CrossRefGoogle Scholar
  8. 8.
    Blake, W.J., Kærn, M., Cantor, C.R., Collins, J.J.: Noise in eukaryotic gene expression. Nature 422, 633–637 (2003)CrossRefGoogle Scholar
  9. 9.
    Cao, Y., Gillespie, D.T., Petzold, L.R.: The slow-scale stochastic simulation algorithm. Journal of Chemical Physics 122(1), 14116 (2005)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
    Cazzaniga, P.: Stochastic algorithms for biochemical processes. Ph.D. thesis, Università degli Studi di Milano-Bicocca (2010)Google Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    Chaouiya, C.: Petri net modelling of biological networks. Briefings in Bioinformatics 8(4), 210–219 (2007)CrossRefGoogle Scholar
  17. 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)CrossRefMATHGoogle Scholar
  18. 18.
  19. 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)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 21.
    Gillespie, D.T.: General method for numerically simulating stochastic time evolution of coupled chemical-reactions. Journal of Computational Physics 22, 403–434 (1976)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry 81(25), 2340–2361 (1977)CrossRefGoogle Scholar
  23. 23.
    Gillespie, D.T.: Approximate accelerated stochastic simulation of chemically reacting systems. Journal of Chemical Physics 115(4), 1716–1733 (2001)CrossRefGoogle Scholar
  24. 24.
    Gillespie, D.T.: Stochastic simulation of chemical kinetics. Annual Review of Physical Chemistry 58, 35–55 (2007)CrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 26.
  27. 27.
    Gunawan, R., Cao, Y., Petzold, L., Doyle, F.J.: Sensitivity analysis of discrete stochastic systems. Biophysical Journal 88, 2530–2540 (2005)CrossRefGoogle Scholar
  28. 28.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  29. 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)CrossRefGoogle Scholar
  30. 30.
    The Infobiotic Web Page, http://www.infobiotic.org/
  31. 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. 32.
    Klipp, E., Liebermeister, W., Wierling, C., Kowald, A., Lehrach, H., Herwig, R.: Systems Biology: A Textbook. Wiley, Chichester (2009)Google Scholar
  33. 33.
    Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)MATHGoogle Scholar
  34. 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)CrossRefGoogle Scholar
  35. 35.
    Leporati, A., Besozzi, D., Cazzaniga, P., Pescini, D., Ferretti, C.: Computing with energy and chemical reactions. Natural Computing 9(2), 493–512 (2010)MathSciNetCrossRefMATHGoogle Scholar
  36. 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)CrossRefGoogle Scholar
  37. 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)CrossRefGoogle Scholar
  38. 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)CrossRefGoogle Scholar
  39. 39.
    McAdams, H., Arkin, A.: Stochastic mechanisms in gene expression. Proceedings of the National Academy of Sciences 94(3), 814–819 (1997)CrossRefGoogle Scholar
  40. 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. 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)CrossRefGoogle Scholar
  42. 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)CrossRefGoogle Scholar
  43. 43.
    The MP Virtual Laboratory, http://mplab.scienze.univr.it/
  44. 44.
    The MPI standard Web Page, http://www-unix.mcs.anl.gov/mpi/
  45. 45.
  46. 46.
    The P Systems Web Page, http://ppage.psystems.eu/
  47. 47.
    Păun, G.: Computing with membranes. Journal of Computer and System Sciences 61(1), 108–143 (2000)MathSciNetCrossRefMATHGoogle Scholar
  48. 48.
    Păun, G., Rozenberg, G., Salomaa, A. (eds.): The Oxford Handbook of Membrane Computing. Oxford University Press, Oxford (2010)MATHGoogle Scholar
  49. 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)MathSciNetCrossRefMATHGoogle Scholar
  50. 50.
    Plyasunov, S., Arkin, A.: Efficient stochastic sensitivity analysis of discrete event systems. Journal of Computational Physics 221, 724–738 (2007)MathSciNetCrossRefMATHGoogle Scholar
  51. 51.
    Pomerening, J.R.: Uncovering mechanisms of bistability in biological systems. Current Opinion in Biotechnology 19(4), 381–388 (2008)CrossRefGoogle Scholar
  52. 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)CrossRefGoogle Scholar
  53. 53.
  54. 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)CrossRefGoogle Scholar
  55. 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. 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. 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)MATHGoogle Scholar
  58. 58.
    Saltelli, A., Ratto, M., Tarantola, S., Campolongo, F.: Sensitivity analysis for chemical models. Chemical Reviews 105, 2811–2827 (2005)CrossRefMATHGoogle Scholar
  59. 59.
    The SBML portal, http://www.sbml.org/
  60. 60.
    Szallasi, Z., Stelling, J., Periwal, V.: Systems Modeling in Cellular Biology. The MIT Press, Cambridge (2006)CrossRefMATHGoogle Scholar
  61. 61.
    Turner, T.E., Schnell, S., Burrage, K.: Stochastic approaches for modelling in vivo reactions. Computational Biology and Chemistry 28, 165–178 (2004)CrossRefMATHGoogle Scholar
  62. 62.
    Tyson, J.J.: Some further studies of nonlinear oscillations in chemical systems. Journal of Chemical Physics 58, 3919–3930 (1973)CrossRefGoogle Scholar
  63. 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)CrossRefGoogle Scholar
  64. 64.
    Wadhams, G.H., Armitage, J.P.: Making sense of it all: bacterial chemotaxis. Nature Reviews Molecular Cell Biology 5(12), 1024–1037 (2004)CrossRefGoogle Scholar
  65. 65.
    Widder, S., Macía, J., Solé, R.: Monomeric bistability and the role of autoloops in gene regulation. PloS One 4(4), e5399 (2009)CrossRefGoogle Scholar
  66. 66.
    Wilhelm, T.: The smallest chemical reaction system with bistability. BMC Systems Biology 3(1), 90 (2009)CrossRefGoogle Scholar
  67. 67.
    Wilkinson, D.J.: Stochastic Modelling for Systems Biology. Chapman & Hall, Boca Raton (2006)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Daniela Besozzi
    • 1
  • Paolo Cazzaniga
    • 2
  • Giancarlo Mauri
    • 2
  • Dario Pescini
    • 2
  1. 1.Dipartimento di Informatica e ComunicazioneUniversità degli Studi di MilanoMilanoItaly
  2. 2.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversità degli Studi di Milano-BicoccaMilanoItaly

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