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The Many Roads to the Simulation of Reaction Systems

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Reaction systems are a computational model inspired by the bio-chemical reactions that happen inside biological cells. They have been and currently are studied for their many nice theoretical properties. They are also a useful modeling tool for biochemical systems, but in order to be able to employ them effectively in the field the presence of efficient and widely available simulators is essential. Here we explore three different algorithms and implementations of the simulation, comparing them to the current state of the art. We also show that we can obtain performances comparable to GPU-based simulations on real-world systems by using a carefully tuned CPU-based simulator.
Wydawca
Rocznik
Strony
175--188
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
  • Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy
  • Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy
autor
  • Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy
  • Aix Marseille Université, Université de Toulon, CNRS, LIS, Marseille, France
Bibliografia
  • [1] Ehrenfeucht A, Rozenberg G. Basic notions of reaction systems. In: Calude CS, Calude E, Dinneen MJ (eds.), Developments in Language Theory, 8th International Conference, DLT 2004, volume 3340 of Lecture Notes in Computer Science, pp. 27-29. Springer, 2005. URL https://doi.org/10.1007/978-3-540-30550-7_3.
  • [2] Ehrenfeucht A, Rozenberg G. Reaction systems. Fundamenta Informaticae, 2007. 75:263-280. URL http://iospress.metapress.com/content/b86t11hryvwq69l0/.
  • [3] Demongeot J, Noual M, Sené S. On the number of attractors of positive and negative Boolean automata circuits. In: 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops. IEEE, 2010 pp. 782-789.
  • [4] Demongeot J, Noual M, Sené S. Combinatorics of Boolean automata circuits dynamics. Discrete Applied Mathematics, 2012. 160(4-5):398-415.
  • [5] Kari J. Theory of cellular automata: A survey. Theoretical Computer Science, 2005. 334(1-3):3-33.
  • [6] Dennunzio A, Lena PD, Formenti E, Margara L. Periodic Orbits and Dynamical Complexity in Cellular Automata. Fundamenta Informaticae, 2013. 126(2-3):183-199. doi:10.3233/FI-2013-877.
  • [7] Ehrenfeucht A, Main M, Rozenberg G. Combinatorics of life and death for reaction systems. International Journal of Foundations of Computer Science, 2010. 21(03):345-356.
  • [8] Dennunzio A, Formenti E, Manzoni L. Reaction systems and extremal combinatorics properties. Theoretical Computer Science, 2015. 598:138-149.
  • [9] Dennunzio A, Formenti E, Manzoni L, Porreca AE. Ancestors, descendants, and gardens of Eden in reaction systems. Theoretical Computer Science, 2015. 608(1):16-26. URL https://doi.org/10.1016/j.tcs.2015.05.046.
  • [10] Azimi S, Gratie C, Ivanov S, Manzoni L, Petre I, Porreca AE. Complexity of model checking for reaction systems. Theoretical Computer Science, 2016. 623:103-113. URL https://doi.org/10.1016/j.tcs.2015.11.040.
  • [11] Dennunzio A, Formenti E, Manzoni L, Margara L, Porreca AE. Complexity of the dynamics of reaction systems. Information and Computation, 2019. doi:10.1016/j.ic.2019.03.006. In press.
  • [12] Barbuti R, Gori R, Levi FL, Milazzo P. Specialized Predictor for Reaction Systems with Context Properties. Fundamenta Informaticae, 2016. 147(2-3):173-191. doi:10.3233/FI-2016-1403. URL https://doi.org/10.3233/FI-2016-1403.
  • [13] Barbuti R, Gori R, Levi FL, Milazzo P. Investigating dynamic causalities in reaction systems. Theoretical Computer Science, 2016. 623:114-145. doi:10.1016/j.tcs.2015.11.041. URL https://doi.org/10.1016/j.tcs.2015.11.041.
  • [14] Manzoni L, Poças D, Porreca AE. Simple reaction systems and their classification. International Journal of Foundations of Computer Science, 2014. 25(4):441-457. URL https://doi.org/10.1142/S012905411440005X.
  • [15] Corolli L, Maj C, Marini F, Besozzi D, Mauri G. An excursion in reaction systems: From computer science to biology. Theoretical Computer Science, 2012. 454:95-108. URL https://doi.org/10.1016/j.tcs.2012.04.003.
  • [16] Azimi S, Bogdan I, Petre I. Reaction system models for the heat shock response. Fundamenta Informaticae, 2014. 131(3-4):299-312. URL https://doi.org/10.3233/FI-2014-1016.
  • [17] Azimi S, Gratie C, Ivanov S, Petre I. Dependency graphs and mass conservation in reaction systems. Theoretical Computer Science, 2015. 598:23-39.
  • [18] brsim GitHub Repository. https://github.com/scolobb/brsim/, 2014.
  • [19] brsim Web Interface. http://combio.abo.fi/research/reaction-systems/reaction-system-simulator/.
  • [20] Ivanov S, Rogojin V, Azimi S, Petre I. WEBRSIM: AWeb-Based Reaction Systems Simulator. In: Enjoying Natural Computing, pp. 170-181. Springer, 2018.
  • [21] Nobile MS, Porreca AE, Spolaor S, Manzoni L, Cazzaniga P, Mauri G, Besozzi D. Efficient simulation of reaction systems on Graphics Processing Units. Fundamenta Informaticae, 2017. 154(1-4):307-321. https://doi.org/10.3233/FI-2017-1568.
  • [22] HERESY GitHub Repository. https://github.com/aresio/HERESY/, 2017.
  • [23] Steele G. Common LISP: the language. Butterworth-Heinemann, 1990.
  • [24] cl-rs Github Repository. https://github.com/mnzluca/cl-rs.
  • [25] Helikar T, Kochi N, Kowal B, Dimri M, Naramura M, Raja SM, Band V, Band H, Rogers JA. A comprehensive, multi-scale dynamical model of ErbB receptor signal transduction in human mammary epithelial cells. PLoS ONE, 2013. 8(4):e61757.
  • [26] Numpy webpage. http://www.numpy.org/.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5273057a-9973-4bb2-a1c5-7803d3fa54b7
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