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2018 | Vol. 160, nr 1/2 | 167--179
Tytuł artykułu

Stochastic Simulation-based Prediction of the Behavior of the p16-mediated Signaling Pathway

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this work we use hybrid Petri nets to create a model of the p16-mediated signaling pathway in higher eukaryotes and conduct its stochastic simulation-based validation by wet lab observations available from literature. The validation is conducted in terms of stochastic simulations with respect to the wild-type p16 protein and its mutated form. Our model catches the behavior of the major molecular regulators of the p16-mediated signaling pathway in wild-type cells as well as when DNA damage is detected or replicative senescence occurs. We observe that the stochastic model predicts some characteristics of the underlying pathway more clearly, evidently and perspicuously compared to the deterministic model, enriching the breadth and the quality of the outcome.
Wydawca

Rocznik
Strony
167--179
Opis fizyczny
Bibliogr. 35 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Applied Mathematics and Computer Science, Faculty of Arts and Sciences, Eastern Mediterranean University, Famagusta, North Cyprus, Mersin-10, Turkey, rza.bashirov@emu.edu.tr
  • Faculty of Medicine, Eastern Mediterranean University, Famagusta, North Cyprus, Mersin-10, Turkey, ilke.cetin@emu.edu.tr
Bibliografia
  • [1] Akçay Nİ, Bashirov R, Tüzmen S¸ . Validation of signalling pathways: case study of the p16-mediated pathway. J Bioinform Comput Biol. 2015; 13(2):1550007. doi:10.1142/S0219720015500079.
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  • [3] Bird RC. Role of Cyclins and Cyclin-dependent kinases in G1 phase progression. In: G1 Phase Progression, Boonstra J (ed.), Kluwer Academic, New York; 2003. p. 40-57.
  • [4] Boonstra J. Restriction points to the G1 phase to the mammalian cell cycle. In: G1 Phase Progression, Boonstra J (ed.), Kluwer Academic, New York; 2003. p. 1-7.
  • [5] Csikasz-Nagy A. Computational systems biology of the cell cycle. Brief Bioinform. 2009; 10:424-434.
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  • [9] Heiner M, Gilbert D, Donaldson R. Petri Nets for Systems and Synthetic Biology. In: 8th international conference on formal methods for computational systems biology, Conference Proceedings. LNCS 5016, Springer; 2008. p. 215-264.
  • [10] Heiner M, Herajy M, Liu F, et al. Snoopy a unifying Petri net tool. LNCS. 2012; 7347:398-407.
  • [11] Herajy M, Schwarick M, Heiner M. Hybrid Petri nets for modelling the eukaryotic cell cycle. ToPNoC VIII, LNCS. 2013; 8100:121-141.
  • [12] Liu F, Heiner M. Fuzzy stochastic Petri sets for modeling biological systems with uncertain kinetic parameters. PLoS ONE. 2016; 11(2):e0149674. doi:10.1371/journal.pone.0149674.
  • [13] Ko MS, Nakauchi H, Takahashi N. The dose dependence of glucocorticoid-inducible gene expression results from changes in the number of transcriptionally active templates. EMBO J. 1990; 9:2835-2842.
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  • [15] Lamb TD. Gain and kinetics of activation in the G-protein cascade of phototransduction. Proc Natl Acad Sci USA. 1996; 93:566-570.
  • [16] Li S, et al. A quantitative study of the cell division cycle of Cauloubacter crescentus stalked cells. PLoS Comput Biol. 2008; 4:e64.
  • [17] Liu Y, et al. Expression of p16(INK4a) in peripheral blood T-cells is a biomarker of human aging. Aging Cell. 2009; 8:439-448.
  • [18] Lygeros J, et al. Stochastic hybrid modeling of DNA replication across a complete genome. Proc Natl Acad Sci USA. 2008; 105:12295-12300.
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  • [20] Meng TC, Somani S, Dhar P. Modeling and simulation of biological systems with stochasticity. In Silico Biol. 2004; 4:293-309.
  • [21] Moeller SJ, Shearff RJ. G1 phase: components, conundrums, context, in cell cycle regulation. Kaldis P (ed.), Springer-Verlag, Berlin, Heidelberg, 2005.
  • [22] Mura I, Csikasz-Nagy A. Stochastic Petri net extensions of a yeast cell cycle model. J Theor Biol. 2008; 254:850-860.
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  • [25] Qu Z, Weiss JN, et al. Regulation of the mammalian cell cycle: a model of the G1-to-S transition. Am J Physiol Cell Physiol. 2013; 254:344-364.
  • [26] Rayess H, Wang MB, Srivatsan ES. Cellular senescence and tumor suppressor gene p16. Int J Cancer. 2012; 139:1715-1725.
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  • [31] Sriram K, et al. A minimal mathematical model combining several regulatory cycles from the budding yeast cell cycle. IETSyst Biol. 2007; 1:326-341.
  • [32] Stacey DW. Three observations that have changed our understanding of Cyclin D1 and p27 in cell cycle control. Genes and Cancer. 2010; 12:1189-1199.
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  • [34] Yang X, Han R, Guo Y, Bradley J, Cox B, Dickinson R, et al. Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA. BMC Bioinformatics. 2011;13(1-17).
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Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
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