PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Adaptive and Bio-semantics of Continuous Petri Nets : Choosing the Appropriate Interpretation

Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Continuous Petri nets (CPN) provide a graphical tool to model and analyse the deterministic dynamic behaviour of biological reaction networks. They can be considered as an alternative to the traditional ODE representation of biological models, enjoying a visual depiction of reaction networks. A model constructed as CPN can take advantages of quantitative (e.g., transient and steady state analysis) as well as qualitative (e.g., structural analysis) techniques. However, there are different semantics of CPN due to varying interpretations of transition rates. Choosing an appropriate semantics and corresponding simulator is not a straightforward procedure for the modelling of certain biological systems. In this paper, we compare two widely used semantics of CPN: adaptive semantics and bio-semantics. In the adaptive case, the enabling of continuous transitions may vary and the ODEs are correspondingly adjusted during model execution in order to avoid negative markings, while continuous transitions are always enabled in the bio-semantics and ODEs are never altered during the whole simulation period. We discuss the implementation complexity of both approaches in the context of systems biology and present two case studies to illustrate the best utilisation and individual strength of the two interpretations.
Wydawca
Rocznik
Strony
53--80
Opis fizyczny
Bibliogr. 41 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Mathematics and Computer Science, Faculty of Science, Port Said University, 42521 - Port Said, Egypt
  • Computer Science Institute, Brandenburg University of Technology, Postbox 10 13 44, 03013 Cottbus, Germany
autor
  • Computer Science Institute, Brandenburg University of Technology, Postbox 10 13 44, 03013 Cottbus, Germany
Bibliografia
  • [1] Gostner R, Baldacci B, Morine MJ, Priami C. Graphical Modeling Tools for Systems Biology. ACM Comput. Surv., 2014;47(2):16:1-16:21. doi:10.1145/2633461.
  • [2] Keating SM, Novère N. Supporting SBML as a Model Exchange Format in Software Applications. In: In Silico Systems Biology. Humana Press, Totowa, NJ, 2013 pp. 201-225. doi:10.1007/978-1-62703-450-0_11.
  • [3] Murata T. Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 1989;77(4):541-580. doi: 10.1109/5.24143.
  • [4] Gilbert D, Heiner M. From Petri Nets to Differential Equations - An Integrative Approach for Biochemical Network Analysis. In: Donatelli S, Thiagarajan P (eds.), Proc. ICATPN 2006, volume 4024 of LNCS. Springer, 2006 pp. 181-200. doi:10.1007/11767589_11.
  • [5] Heiner M, Gilbert D. How Might Petri Nets Enhance Your Systems Biology Toolkit. In: Kristensen LM, Petrucci L (eds.), Applications and Theory of Petri Nets: 32nd International Conference, PETRI NETS 2011. Springer Berlin Heidelberg, 2011 pp. 17-37. doi:10.1007/978-3-642-21834-7_2.
  • [6] Heiner M, Gilbert D. BioModel engineering for multiscale Systems Biology. Progress in Biophysics and Molecular Biology, 2013;111(23):119-128. URL https://doi.org/10.1016/j.pbiomolbio.2012.10.001.
  • [7] Herajy M, Heiner M. Hybrid Representation and Simulation of Stiff Biochemical Networks. J. Nonlinear Analysis: Hybrid Systems, 2012;6(4):942-959. URL https://doi.org/10.1016/j.nahs.2012.05.004.
  • [8] Herajy M, Schwarick M, Heiner M. Hybrid Petri Nets for Modelling the Eukaryotic Cell Cycle. In: Koutny M, Aalst WMP, Yakovlev A (eds.), Transactions on Petri Nets and Other Models of Concurrency VIII. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013 pp. 123-141. doi:10.1007/978-3-642-40465-8_7.
  • [9] Liu F, Heiner M, Yang M. Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters. PLoS ONE, 2016;11(2):1-19. doi:10.1371/journal.pone.0149674.
  • [10] Matsuno H, Nagasaki M, Miyano S. Hybrid Petri net based modeling for biological pathway simulation. Natural Computing, 2011;10(3):1099-1120. doi:10.1007/s11047-009-9164-6.
  • [11] Heiner M, Herajy M, Liu F, Rohr C, Schwarick M. Snoopy - a unifying Petri net tool. In: Haddad S, Pomello L (eds.), Proc. PETRI NETS 2012, volume 7347 of LNCS. Springer, 2012 pp. 398-407. doi:10.1007/978-3-642-31131-4_22.
  • [12] Ajmone M, Balbo G, Conte G, Donatelli S, Franceschinis G. Modelling with Generalized Stochastic Petri Nets. Wiley Series in Parallel Computing, John Wiley and Sons, 1995. ISBN:0471930598.
  • [13] Gillespie D. Stochastic simulation of chemical kinetics. Annual review of physical chemistry, 2007;58(1):35-55. doi:10.1146/annurev.physchem.58.032806.104637.
  • [14] Alla H, David R. Continuous Petri nets. In: 8th European Workshop on Application and Theory of Petri nets. 1987 pp. 328-336.
  • [15] Alla H, David R. Continuous and Hybrid Petri Nets. Journal of Circuits, Systems and Computers, 1998;08(01):159-188. URL https://doi.org/10.1142/S0218126698000079.
  • [16] David R, Alla H. Discrete, Continuous, and Hybrid Petri Nets. Springer, 2010.
  • [17] Herajy M, Heiner M. Accelerated Simulation of Hybrid Biological Models with Quasi-Disjoint Deterministic and Stochastic Subnets. In: Cinquemani E, Donzé A (eds.), Hybrid Systems Biology: 5th International Workshop, HSB 2016, Grenoble, France, October 20-21, 2016, Proceedings, LNBI. Springer International Publishing. ISBN 978-3-319-47151-8, 2016 pp. 20-38. doi:10.1007/978-3-319-47151-8\_2.
  • [18] Jensen K. Coloured Petri Nets and the Invariant-Method. Theoretical Computer Science, 1981;14(3):317-336. URL https://doi.org/10.1016/0304-3975(81)90049-9.
  • [19] Herajy M, Liu F, Rohr C. Coloured hybrid Petri nets for systems biology. In: Proc. of the 5th International Workshop on Biological Processes & Petri Nets (BioPPN), satellite event of PETRI NETS 2014, volume 1159 of CEUR Workshop Proceedings. CEUR-WS.org, 2014 pp. 60-76.
  • [20] Liu F, Blätke M, Heiner M, Yang M. Modelling and simulating reaction diffusion systems using coloured Petri nets. Computers in Biology and Medicine, 2014;53:297-308. URL https://doi.org/10.1016/j.compbiomed.2014.07.004.
  • [21] Breitling R, Gilbert D, Heiner M, Orton R. A structured approach for the engineering of biochemical network models, illustrated for signalling pathways. Briefings in Bioinformatics, 2008;9(5):404-421. doi:10.1093/bib/bbn026.
  • [22] Soliman S, Heiner M. A Unique Transformation from Ordinary Differential Equations to Reaction Networks. PLoS ONE, 2010;5(12):1-6. doi:10.1371/journal.pone.0014284.
  • [23] Hardy S, Iyengar R. Analysis of Dynamical Models of Signaling Networks with Petri Nets and Dynamic Graphs. In: Koch I, Reisig W, Schreiber F (eds.), Modeling in Systems Biology: The Petri Net Approach. Springer London, London. ISBN 978-1-84996-474-6, 2011 pp. 225-251. doi:10.1007/978-1-84996-474-6\_11.
  • [24] Heiner M, Sriram K. Structural Analysis to Determine the Core of Hypoxia Response Network. PLOS ONE, 2010;5(1):1-17. doi:10.1371/journal.pone.0008600.
  • [25] Angeli D, De Leenheer P, Sontag E. A Petri Net Approach to Persistence Analysis in Chemical Reaction Networks. In: Queinnec I, Tarbouriech S, Garcia G, Niculescu SI (eds.), Biology and Control Theory: Current Challenges. Springer Berlin Heidelberg, Berlin, Heidelberg. ISBN 978-3-540-71988-5, 2007 pp.181-216. doi:10.1007/978-3-540-71988-5\_9.
  • [26] Bail J, Alla H, David R. Asymptotic continuous Petri nets. Discrete Event Dynamic Systems, 1993;2(3):235-263. doi:10.1007/BF01797160.
  • [27] Demongodin I, Koussoulas NT. Differential Petri nets: representing continuous systems in a discrete-event world. IEEE Transactions on Automatic Control, 1998;43(4):573-579. doi:10.1109/9.665073.
  • [28] Silva M, Recalde L. On fluidification of Petri Nets: from discrete to hybrid and continuous models. Annual Reviews in Control, 2004;28(2):253-266. URL https://doi.org/10.1016/j.arcontrol.2004.05.002.
  • [29] Gudino-Mendoza B, López-Mellado E, Alla H. A linear characterization of the switching dynamic behavior of timed continuous Petri nets with structural conflicts. Nonlinear Analysis: Hybrid Systems, 2016;19:38-59. URL https://doi.org/10.1016/j.nahs.2015.07.003.
  • [30] Matsuno H, Tanaka Y, Aoshima H, Doi A, Matsui M, Miyano S. Biopathways representation and simulation on hybrid functional Petri net. In silico biology, 2003;3(3):389-404.
  • [31] Alla H, David R. A modelling and analysis tool for discrete events systems: continuous Petri net. Performance Evaluation, 1998;33(3):175-199. URL https://doi.org/10.1016/S0166-5316(98)00016-9.
  • [32] Dotoli M, Fanti M, Giua A, Seatzu C. First-order hybrid Petri nets. An application to distributed manufacturing systems. Nonlinear Analysis: Hybrid Systems, 2008;2(2):408-430. URL https://doi.org/10.1016/j.nahs.2006.05.005.
  • [33] Voit EO, Martens HA, Omholt SW. 150 Years of the Mass Action Law. PLoS Comput Biol, 2015;11:1-7. URL https://doi.org/10.1371/journal.pcbi.1004012.
  • [34] Mitchell S, Mendes P. A Computational Model of Liver Iron Metabolism. PLoS Comput Biol, 2013;9(11):1-14. doi:10.1371/journal.pcbi.1003299.
  • [35] Wilkinson D. Stochastic Modelling for System Biology. CRC Press, New York, 1st Edition, 2006.
  • [36] Khoury DS, Barron AB, Myerscough MR. Modelling Food and Population Dynamics in Honey Bee Colonies. PLoS ONE, 2013;8(5):e59084. doi:10.1371/journal.pone.0059084.
  • [37] Russell S, Barron AB, Harris D. Dynamic modelling of honey bee (Apis mellifera) colony growth and failure. Ecological Modelling, 2013;265(0):158-169. URL https://doi.org/10.1016/j.ecolmodel.2013.06.005.
  • [38] Khoury D, Myerscough M, Barron A. A quantitative model of honey bee colony population dynamic. PLoS ONE, 2011;6:e18491. URL https://doi.org/10.1371/journal.pone.0018491.
  • [39] Proß S. Hybrid modeling and optimization of biological processes. Ph.D. thesis, Bielefeld University, 2013.
  • [40] Herajy M, Heiner M. A Steering Server for Collaborative Simulation of Quantitative Petri Nets. In: Ciardo G, Kindler E (eds.), Proc. PETRI NETS 2014, volume 8489 of LNCS. Springer, 2014 pp. 374-384. doi:10.1007/978-3-319-07734-5_21.
  • [41] Herajy M, Heiner M. Petri Net-Based Collaborative Simulation and Steering of Biochemical Reaction Networks. Fundamenta Informatica, 2014;129(1-2):49-67. doi:10.3233/FI-2014-960.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6cfede57-064e-4332-b676-7509020ddfbb
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.