Narzędzia help

Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
cannonical link button


Fundamenta Informaticae

Tytuł artykułu

Semi-quantitative Modelling of Gene Regulatory Processes with Unknown Parameter Values Using Fuzzy Logic and Petri Nets

Autorzy Bordon, J.  Moškon, M.  Zimic, N.  Mraz, M. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN Petri nets are a well-established modelling framework in life sciences and have been widely applied to systems and synthetic biology in recent years. With the various extensions they serve as graphical and simulation interface for both qualitative and quantitative modelling approaches. In terms of quantitative approaches, Stochastic and Continuous Petri nets are extensively used for modelling biological system’s dynamics if underlying kinetic data are known. However, these are often only vaguely defined or even missing. In this paper we present a fuzzy approach, which can be used to model biological processes with unknown kinetic data in order to still obtain quantitatively relevant simulation results. We define fuzzy firing rate functions, which can be used in Continuous Petri nets and are able to describe different processes that govern the dynamics of gene expression networks. They can be used in combination with the conventional firing rate functions and applied only in the parts of the system for which the kinetic data are missing. The case study of the proposed approach is performed on models of a hypothetical repressilator and Neurospora circadian rhythm.
Słowa kluczowe
EN Petri nets   modelling biological processes   fuzzy logic   unknown kinetic parameter values  
Wydawca IOS Press
Czasopismo Fundamenta Informaticae
Rocznik 2018
Tom Vol. 160, nr 1/2
Strony 81--100
Opis fizyczny Bibliogr. 38 poz., rys., tab., wykr.
autor Bordon, J.
  • Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia,
autor Moškon, M.
  • Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
autor Zimic, N.
  • Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
autor Mraz, M.
  • Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
[1] Murata T. Petri nets: properties, analysis and applications. Proceedings of the IEEE, 1989. 77(4):541-580. doi: 10.1109/5.24143.1104.0291v1, URL
[2] Reddy VN, Liebman MN, Mavrovouniotis ML. Qualitative analysis of biochemical reaction systems. Computers in biology and medicine, 1996. 26(1):9-24. doi:10.1016/0010-4825(95)00042-9. URL
[3] Sackmann A, Heiner M, Koch I, Blume-Jensen P, Hunter T, Wang Y, Dohlman H, Bardwell L, Gustin M, Albertyn J, Alexander M, Davenport K, Ciliberto A, Novak B, Tyson J, Kofahl B, Klipp E, Qu Z, Weiss J, MacLellan W, Yi T, Kitano H, Simon M, Schuster S, Hilgetag C, Schuster R, Klamt S, Saez-Rodriguez J, Lindquist J, Simeoni L, GE D, Simao E, Remy E, Thieffry D, Chaouiya C, Zevedei-Oancea I, Schuster S, Heiner M, Koch I, Will J, Heiner M, Koch I, Will J, Heiner M, Koch I, Petri C, Murata T, Starke P, Reddy V, Mavrovouniotis M, Liebman M, Hofestädt R, Reddy V, Liebman M, Mavrovouniotis M, Koch I, Junker B, Heiner M, Voss K, Heiner M, Koch I, Matsuno H, Tanaka Y, Aoshima H, Doi A, Matsui M, Miyano S, Chen M, Hofestädt R, Doi A, Fujita S, Matsuno H, Nagasaki M, Miyano S, Hardy S, Robillard P, Pinney J, Westhead D, McConkey G, Will J, Heiner M, Dohlman H, Thorner J, Elion E, Qi M, Chen W, Bardwell L, Cook J, Voora D, Baggott D, Martinez A, Thorner J, Hicke L, Zanolari B, Riezman H, Esch R, Errede B, Baumgarten B, Lautenbach K, Zevedei-Oancea I, Schuster S, David R, Alla H, Lonitz K, Gilbert D, Heiner M, Schoeberl B, Eichler-Jonsson C, Gilles E, Muller G, Oda K, Matsuoka Y, Funahashi A, Kitano H, Fieber M. Application of Petri net based analysis techniques to signal transduction pathways. BMC bioinformatics, 2006. 7(1):482. doi:10.1186/1471-2105-7-482. URL
[4] Koch I, Junker BH, Heiner M. Application of Petri net theory for modelling and validation of the sucrose breakdown pathway in the potato tuber. Bioinformatics (Oxford, England), 2005. 21(7):1219-26. doi:10.1093/bioinformatics/bti145. URL
[5] Matsuno H, Doi A, Nagasaki M, Miyano S. Hybrid Petri net representation of gene regulatory network. Pacific Symposium On Biocomputing, 2000. 349(338-349):341-352. doi:10902182.
[6] Chaouiya C, Remy E, Ruet P, Thieffry D. Qualitative modelling of genetic networks: From logical regulatory graphs to standard petri nets. Applications and Theory of Petri Nets 2004, 2004. pp. 137-156.
[7] Heiner M, Gilbert D, Donaldson R. Petri nets for systems and synthetic biology. Formal methods for computational systems biology, 2008. pp. 215-264.
[8] Steggles LJ, Banks R, Shaw O, Wipat A. Qualitatively modelling and analysing genetic regulatory networks: A Petri net approach. Bioinformatics, 2007. 23(3):336-343. doi:10.1093/bioinformatics/btl596.
[9] Comet JPP, Klaudel H, Liauzu S. Modeling multi-valued genetic regulatory networks using high-level Petri nets. Applications and Theory of Petri Nets 2005, 2005. 3536:986.
[10] Banks R, Steggles LJ. A High-Level Petri Net Framework for Genetic Regulatory Networks. Technical Report 3, 2007.
[11] Chaouiya C, Naldi A, Remy E, Thieffry D. Petri net representation of multi-valued logical regulatory graphs. Natural Computing, 2011. 10(2):727-750. doi:10.1007/s11047-010-9178-0. URL
[12] 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. URL
[13] Gilbert D, Heiner M. LNCS 4024 - From Petri Nets to Differential Equations - An Integrative Approach for Biochemical Network Analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006. 4024(December):181-200. doi:10.1007/11767589.
[14] Goss PJE, Peccoud J. Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. PNAS, 1998. 95(12):6750-6755. doi:10.1073/pnas.95.12.6750. URL
[15] Shaw O, Steggles J, Wipat A. Automatic Parameterisation of Stochastic Petri Net Models of Biological Networks. Electronic Notes in Theoretical Computer Science, 2006. 151(3):111-129. doi:10.1016/j.entcs.2006.03.015.
[16] Bordon J, Moškon M, Zimic N, Mraz M. 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):1199-1205. doi:10.1109/TCBB.2015.2424424.
[17] Zadeh LA. Fuzzy logic and approximate reasoning. Synthese, 1975. 30(3):407-428.
[18] Zimmermann HJ. Fuzzy set theory and its applications. Springer Science & Business Media, USA, 2001.
[19] Gendrault Y, Madec M, Wlotzko V, Lallement C, Haiech J. Fuzzy logic, an intermediate description level for design and simulation in synthetic biology. 2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013, 2013. pp. 370-373. doi:10.1109/BioCAS.2013.6679716.
[20] Tepeli A, Hortaçsu 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.
[21] Morris MK, Saez-Rodriguez J, Clarke DC, Sorger PK, Lauffenburger DA. Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS Comput Biol, 2011. 7(3):e1001099-e1001099. doi:10.1371/journal.pcbi.1001099.
[22] Maraziotis IA, Dragomir A, Bezerianos A. Gene networks reconstruction and time-series prediction from microarray data using recurrent neural fuzzy networks. IET Systems Biology, 2007. 1(1):41-50.
[23] Aldridge BB, Saez-Rodriguez J, Muhlich JL, Sorger PK, Lauffenburger DA. Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling. PLoS computational biology Biol, 2009. 5(4):e1000340. doi:10.1371/journal.pcbi.1000340.
[24] Huang Z, Hahn J. Fuzzy modeling of signal transduction networks. Chemical Engineering Science, 2009. 64(9):2044-2056. doi:10.1016/j.ces.2009.01.041. URL
[25] Munoz C, Vargas F, Bustos J, Muñoz C, Vargas F, Bustos J, Curilem M, Salvo S, Miranda H. Fuzzy logic in genetic regulatory network models. Int. J. of Computers, Communications & Control, 2009. IV(4):363-373.
[26] Woolf PJ, Wang Y. A fuzzy logic approach to analyzing gene expression data. Physiological Genomics, 2000. 3(1):9-15.
[27] Brock GN, Pihur V, Kubatko L. Detecting Gene Regulatory Networks from Microarray Data Using Fuzzy Logic. Fuzzy Systems in Bioinformatics and Computational Biology, 2009. pp. 141-163.
[28] Schmidt-Heck W, Matz-Soja M, Aleithe S, Marbach E, Guthke R, Gebhardt R. Fuzzy modeling reveals a dynamic self-sustaining network of the GLI transcription factors controlling important metabolic regulators in adult mouse hepatocytes. Molecular bioSystems, 2015. 11(8):2190-2197. doi:10.1039/c5mb00129c. URL
[29] Linden R, Bhaya A. Evolving fuzzy rules to model gene expression. BioSystems, 2007. 88(1-2):76-91. doi:10.1016/j.biosystems.2006.04.006.
[30] Hamed RI. Intelligent method of Petri net formal computational modeling of biological networks. In: Computer Science and Electronic Engineering Conference (CEEC), 2013 5th. 2013 pp. 162-167. doi:10.1109/CEEC.2013.6659465.
[31] Küffner R, Petri T, Windhager L, Zimmer R. Petri nets with fuzzy logic (PNFL): reverse engineering and parametrization. PLoS ONE, 2010. 5(9):1-10. doi:10.1371/journal.pone.0012807.
[32] Fryc B, Pancerz K, Peters JF, Suraj Z. On fuzzy reasoning using matrix representation of extended fuzzy Petri nets. Fundamenta Informaticae, 2004. 60(1-4):143-157.
[33] Suraj Z. A new class of fuzzy Petri nets for knowledge representation and reasoning. Fundamenta Informaticae, 2013. 128(1-2):193-207.
[34] 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. URL
[35] Strelkowa N, Barahona M. Switchable genetic oscillator operating in quasi-stable mode. Journal of The Royal Society Interface, 2010. 7(48):1071-1082. doi:10.1098/rsif.2009.0487. 0909.1935.
[36] Elowitz MB, Leibler S, Michael B Elowitz SL. A synthetic oscillatory network of transcriptional regulators. Nature, 2000. 403(6767):335-338. doi:10.1038/35002125.
[37] MATLAB. version 8.50 (R2015a). The MathWorks Inc., Natick, Massachusetts, 2015.
[38] Leloup JC, Gonze D, Goldbeter A. Limit Cycle Models for Circadian Rhythms Based on Transcriptional Regulation in Drosophila and Neurospora. Journal of Biological Rhythms, 1999. 14(6):433-448. doi:10.1177/074873099129000948. URL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-da6b357a-a1c3-43e4-b3a6-8a5711ae1e25
DOI 10.3233/FI-2018-1675