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Petri Net-Based Collaborative Simulation and Steering of Biochemical Reaction Networks

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Języki publikacji
EN
Abstrakty
EN
Computational steering is an interactive remote control of a long running application. The user can adopt it, e.g., to adjust simulation parameters on the fly. Simulation of large-scale biochemical networks is often computationally expensive, particularly stochastic and hybrid simulation. Such extremely time-consuming computations necessitate an interactive mechanism to permit users to try different paths and ask 'what-if-questions' while the simulation is in progress. Furthermore, with the progress of computational modelling and the simulation of biochemical networks, there is a need to manage multi-scale models, which may contain species or reactions at different scales. In this context, Petri nets are of special importance, since they provide an intuitive visual representation of reaction networks. In this paper, we introduce a framework and its implementation for combining Petri nets and computational steering for the representation and interactive simulation of biochemical networks. The main merits of the developed framework are: intuitive representation of biochemical networks by means of Petri nets, distributed collaborative and interactive simulation, and tight coupling of simulation and visualisation.
Wydawca
Rocznik
Strony
49--67
Opis fizyczny
Bibliogr. 32 poz., rys.
Twórcy
autor
  • Department of Mathematics and Computer Science, Faculty of Science, Port Said University, 42521 - Port Said, Egypt
autor
  • Computer Science Institute, Brandenburg University of Technology, D-03013 Cottbus, Germany
Bibliografia
  • [1] Snoopy website, http://www-dssz.informatik.tu-cottbus.de/DSSZ/Software/Snoopy, 2012, Accessed: 20/12/2012.
  • [2] David, R., Alla, H.: Discrete, Continuous, and Hybrid Petri Nets, Springer, 2010.
  • [3] Funahashi, A., Matsuoka, Y., Jouraku, A., Morohashi, M., Kikuchi, N., Kitano, H.: Cell Designer 3.5: AVersatile Modeling Tool for Biochemical Networks, Proceedings of the IEEE, 96(8), 2008, 1254 – 1265.
  • [4] Geist, G., Kohl, J., Papadopoulos, P.: CUMULVS: Providing Fault-Tolerance, Visualization and Steering of Parallel Applications, Journal of High Performance Computing Applications, 11(3), 1997, 224–236.
  • [5] Gilbert, D., Heiner, M.: From Petri nets to differential equations - an integrative approach for biochemical network analysis, in: Petri Nets and Other Models of Concurrency - ICATPN 2006, Lecture Notes in Computer Science (S. Donatelli, P. Thiagarajan, Eds.), vol. 4024, Springer, 2006, 181–200.
  • [6] Gillespie, D.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, J. Comput. Phys., 22(4), 1976, 403 – 434.
  • [7] Gillespie, D.: Exact stochastic simulation of coupled chemical reactions, J. Phys. Chem., 81(25), 1977, 2340–2361.
  • [8] Gillespie, D.: Stochastic simulation of chemical kinetics., Annu Rev Phys Chem., 58(1), 2007, 35–55.
  • [9] Heiner, M., Gilbert, D., Donaldson, R.: Petri Nets for Systems and Synthetic Biology, in: Formal Methods for Computational Systems Biology (M. Bernardo, P. Degano, G. Zavattaro, Eds.), vol. 5016 of Lecture Notes in Computer Science, Springer, 2008, 215–264.
  • [10] Heiner, M., Herajy, M., Liu, F., Rohr, C., Schwarick, M.: Snoopy – A Unifying Petri Net Tool, in: Application and Theory of Petri Nets (S. Haddad, L. Pomello, Eds.), vol. 7347 of Lecture Notes in Computer Science, Springer, 2012, 398–407.
  • [11] Hellander, A., Lötstedt, P.: Hybrid method for the chemical master equation, J. Comput. Phys., 227, 2007, 100–122.
  • [12] Herajy, M.: Computational Steering of Multi-Scale Biochemical Reaction Networks, Ph.D. Thesis, BrandenburgUniversity of Technology Cottbus - Computer Science Institute, 2013.
  • [13] Herajy, M., Heiner, M.: Hybrid representation and simulation of stiff biochemical networks, Nonlinear Analysis: Hybrid Systems, 6(4), 2012, 942–959.
  • [14] Herajy, M., Heiner, M.: Snoopy Computational Steering Framework - User Manual, Technical Report2/2013, Brandenburg University of Technology Cottbus, Dept. of CS, 2013.
  • [15] Herajy, M., Schwarick, M., Heiner, M.: Hybrid Petri Nets for Modelling the Eukaryotic Cell Cycle, ToPNoCVIII, LNCS 8100, 2013, 123–141.
  • [16] Hindmarsh, A., Brown, P., Grant, K., Lee, S., Serban, R., Shumaker, D., Woodward, C.: SUNDIALS: Suite of Nonlinear and Differential/Algebraic Equation Solvers, ACM Trans. Math. Softw., 31, 2005, 363–396.
  • [17] Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., Kummer, U.:COPASI — a COmplex PAthway SImulator, Bioinformatics, 22, 2006, 3067–74.
  • [18] Jose, J., Hao, H., Naama, N., Stanislas, S.: Mechanisms of noise-resistance in genetic oscillators, Proceedings of the National Academy of Sciences of the United States of America, 99(9), 2002, 5988–5992.
  • [19] Kitano, H.: Systems Biology: A Brief Overview, Science, 295(5560), 2002, 1662–1664.
  • [20] Kitano, H., Ghosh, S., Matsuoka, Y.: Social engineering for virtual ’big science’ in systems biology., Nature chemical biology, 7(6), 2011, 323–326.
  • [21] Liu, F.: Colored Petri Nets for Systems Biology, Ph.D. Thesis, Brandenburg University of Technology Cottbus - Computer Science Institute, 2012.
  • [22] Mann, V., Matossian, V., Muralidhar, R., Parashar, M.: DISCOVER: An environment for Web-based interaction and steering of high-performance scientific applications, Concurrency and Computation: Practice andExperience, 13, 2001, 737–754.
  • [23] Marwan, W., Rohr, C., Heiner, M.: Petri nets in Snoopy: A unifying framework for the graphical display, computational modelling, and simulation of bacterial regulatory networks, vol. 804 of Methods in Molecular Biology, chapter 21, Humana Press, 2012, 409–437.
  • [24] Matsuno, H., Tanaka, Y., Aoshima, H., Doi, A., Matsui, M., Miyano, S.: Biopathways representation and simulation on hybrid functional Petri net, In silico biology, 3(3), 2003.
  • [25] Moraru, I., Schaff, J., Slepchenko, B., Blinov, M., Morgan, F., Lakshminarayana, A., Gao, F., Li, Y., Loew,L.: Virtual Cell modelling and simulation software environment, IET Syst Biol., 2(5), 2008, 352–62.
  • [26] Murata, T.: Petri nets: Properties, analysis and applications, Proceedings of the IEEE, 77(4), April 1989, 541–580.
  • [27] Parker, S., Johnson, C., Beazley, D.: Computational steering: Software systems and strategie, IEEE Computational Science Engineering, 4(4), 1997, 50–59.
  • [28] Pickles, S., Haines, R., Pinning, R., Porter, A.: A Practical Toolkit for Computational Steering, Phil Trans.R. Soc., 363, 2005, 1843–1853.
  • [29] Ramsey, S., Orrell, D., Bolouri, H.: Dizzy: stochastic simulation of large-scale genetic regulatory networks.,J Bioinform Comput Bio, 3(2), 2005, 415–36.
  • [30] Shu, J., Watson, L., Ramakrishnan, N., Kamke, F., Deshpande, S.: Computational Steering in the Problem Solving Environment WBCSim, Engineering Computations, 28(7), 2011, 888 – 911.
  • [31] Soliman, S., Heiner, M.: A Unique Transformation from Ordinary Differential Equations to Reaction Networks, PLoS ONE, 5(12), 12 2010, e14284.
  • [32] Vetter, J., Schwan, K.: Models for Computational Steering, Proceedings of the 3rd International Conferenceon Configurable Distributed Systems, IEEE Computer Society, Washington, USA, 1996
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a1318257-f540-4d13-a869-89c1474ceecc
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