PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Colored Fuzzy Petri Nets for Dealing with Genetic Regulatory Networks

Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Fuzzy approaches play an important role in the modeling of genetic regulatory networks (GRNs) with incomplete quantitative data. However, current fuzzy approaches such as fuzzy logic and fuzzy Petri nets (FPNs) can neither clearly describe causal relationships between genes as each interaction between genes is represented by a couple of fuzzy rules, nor easily deal with large GRNs. To address these issues, this paper presents a new class of colored fuzzy Petri nets (CFPNs) by combining colored Petri nets with FPNs, which makes it possible to clearly represent interactions among genes or to construct a compact model for a large GRN requiring many fuzzy rules. We give the definition of CFPNs and a simulation approach which incorporates a reasoning algorithm, as well as a detailed procedure for modeling and analyzing GRNs with CFPNs. We illustrate our approach using a simple example comprising six genes.
Wydawca
Rocznik
Strony
101--118
Opis fizyczny
Bibliogr. 26 poz., rys., tab., wykr.
Twórcy
autor
  • Control and Simulation Center, Harbin Institute of Technology, Harbin 150080, China
autor
  • Control and Simulation Center, Harbin Institute of Technology, Harbin 150080, China
Bibliografia
  • [1] Du P, Gong J, Wurtele ES, Dickerson JA. Modeling gene expression networks using fuzzy logic. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2005. 35(6):1351-1359. doi:10.1109/TSMCB.2005.855590.
  • [2] Shmulevich I, Dougherty ER, Zhang W. From Boolean to probabilistic Boolean networks as models of genetic regulatory networks. Proceedings of the IEEE, 2002. 90(11):1778-1792. doi:10.1109/JPROC.2002.804686.
  • [3] Ibrahim Z, Ngom A, Tawfik AY. Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011. 8(2):326-334. doi:10.1109/TCBB.2010.98.
  • [4] Qazlan TAA, Hamdi-Cherif A, Kara-Mohamed C. State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference. The Scientific World Journal, 2015. 2015. doi:10.1155/2015/148010.
  • [5] Brock GN, Pihur V, Kubatko L. Detecting gene regulatory networks from microarray data using fuzzy logic. In: Fuzzy Systems in Bioinformatics and Computational Biology, Y. Jin and L. Wang, Eds., vol. 242. 2009 p. 141-163.
  • [6] Bastos G, Guimaraes KS. A simpler Bayesian network model for genetic regulatory network inference. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., volume 1. 2005 pp. 304-309 vol. 1. doi:10.1109/IJCNN.2005.1555847.
  • [7] Zadeh L. Fuzzy Sets. Inform. and Control, 1965. (8):338-353.
  • [8] Ressom H, Wang D, Varghese RS, Reynolds R. Fuzzy logic-based gene regulatory network. In: Fuzzy Systems, 2003. FUZZ ’03. The 12th IEEE International Conference on, volume 2. 2003 pp. 1210-1215 vol.2. doi:10.1109/FUZZ.2003.1206604.
  • [9] Sun Y, Feng G, Cao J. A New Approach to Dynamic Fuzzy Modeling of Genetic Regulatory Networks. IEEE Transactions on NanoBioscience, 2010. 9(4):263-272. doi:10.1109/TNB.2010.2082559.
  • [10] Bordon J, Moškon M, Zimic N, Mraz M. A Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015. 12(5):137-148. doi:10.1109/TCBB.2015.2424424.
  • [11] Tepeli A, Hortacsu A. A fuzzy logic approach for regulation in flux balance analysis. Biochemical Engineering Journal, 2008. 39(1):137-148. doi:10.1016/j.bej.2007.08.022.
  • [12] Hamed RI, Ahson S, Parveen R. A new approach for modelling gene regulatory networks using fuzzy Petri nets. Journal of Integrative bioinformatics, 2010. 7(1):113.
  • [13] Lukas Windhager. Modeling of dynamic systems with Petri nets and fuzzy logic, PhD thesis. http://oai:edoc.ub.uni-muenchen.de:15655. Accessed: 2016-09-29.
  • [14] Jensen K. Coloured Petri Nets and the Invariant-Method. Theoretical Computer Science, 1981. 14(3):317-336.
  • [15] Liu F. Colored Petri Nets for Systems Biology, PhD thesis, Brandenburg University of Technology Cottbus, 2012. http://opus.kobv.de/btu/volltexte/2012/2365/pdf/LiuFei_Thesis_final.pdf. Accessed: 2016-09-29.
  • [16] Liu F, Heiner M, Yang M. An efficient method for unfolding colored Petri nets. In: Proceedings of the 2012 Winter Simulation Conference (WSC). 2012 pp. 1-12. doi:10.1109/WSC.2012.6465203.
  • [17] Yeung DS, Ysang ECC. A multilevel weighted fuzzy reasoning algorithm for expert systems. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1998. 28(2):149-158. doi:10.1109/3468.661144.
  • [18] Sun J, Qin SY, Song YH. Fault diagnosis of electric power systems based on fuzzy Petri nets. IEEE Transactions on Power Systems, 2004. 19(4):2053-2059. doi:10.1109/TPWRS.2004.836256.
  • [19] Chen SM, Ke JS, Chang JF. Knowledge representation using fuzzy Petri nets. IEEE Transactions on Knowledge and Data Engineering, 1990. 2(3):311-319.
  • [20] Gao M, Zhou M, Huang X, Wu Z. Fuzzy reasoning Petri nets. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2003. 33(3):314-324.
  • [21] Gao Q, Gilbert D, Heiner M, Liu F, Maccagnola D, Tree D. Multiscale modeling and analysis of planar cell polarity in the Drosophila wing. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2013. 10(2):337-351. doi:10.1109/TCBB.2012.101.
  • [22] Heiner M, Herajy M, Liu F, Rohr C, Schwarick M. Snoopy a unifying Petri net tool. In: Proc. PETRI NETS 2012, volume 7347 of LNCS. Springer, 2012 p. 398407. doi:10.1007/978-3-642-31131-4\_22. URL http://www.springerlink.com/content/l27w488386w03m45/.
  • [23] Jung SH, Cho KH. Reconstruction of Gene Regulatory Networks by Neuro-fuzzy Inference Systems. In: Frontiers in the Convergence of Bioscience and Information Technologies, 2007. FBIT 2007. 2007 pp.32-37. doi:10.1109/FBIT.2007.53.
  • [24] Lee CC. Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Transactions on Systems, Man, and Cybernetics, 1990. 20(2):404-418. doi:10.1109/21.52551.
  • [25] Boyle AP, Araya CL, Brdlik C, Cayting P, Chao Cheng YC, et al. Comparative analysis of regulatory information and circuits across distant species. Nature, 2014. 512:453456. doi:10.1038/nature13668.
  • [26] Liu F, Heiner M, Yang M. Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters. PLoS ONE, 2016. 11(2):e0149674. doi:10.1371/journal.pone.0149674.
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-30dd2080-0a79-47e7-b02f-2d44d6dc84da
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.