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Nonlinear signal transduction network with multistate |
Han-Yu Jiang(姜寒玉) and Jun He(何军)† |
School of Physics and Technology, Nanjing Normal University, Nanjing 210097, China |
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Abstract Signal transduction is an important and basic mechanism to cell life activities. The stochastic state transition of receptor induces the release of signaling molecular, which triggers the state transition of other receptors. It constructs a nonlinear sigaling network, and leads to robust switchlike properties which are critical to biological function. Network architectures and state transitions of receptor affect the performance of this biological network. In this work, we perform a study of nonlinear signaling on biological polymorphic network by analyzing network dynamics of the Ca2+-induced Ca2+ release (CICR) mechanism, where fast and slow processes are involved and the receptor has four conformational states. Three types of networks, Erdös-Rényi (ER) network, Watts-Strogatz (WS) network, and BaraBási-Albert (BA) network, are considered with different parameters. The dynamics of the biological networks exhibit different patterns at different time scales. At short time scale, the second open state is essential to reproduce the quasi-bistable regime, which emerges at a critical strength of connection for all three states involved in the fast processes and disappears at another critical point. The pattern at short time scale is not sensitive to the network architecture. At long time scale, only monostable regime is observed, and difference of network architectures affects the results more seriously. Our finding identifies features of nonlinear signaling networks with multistate that may underlie their biological function.
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Received: 01 June 2021
Revised: 02 August 2021
Accepted manuscript online: 27 August 2021
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PACS:
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87.18.Mp
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(Signal transduction networks)
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87.16.Xa
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(Signal transduction and intracellular signaling)
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87.17.Aa
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(Modeling, computer simulation of cell processes)
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Fund: Project supported by the National Natural Science Foundation of China (Grant No. 11675228) and China Postdoctoral Science Foundation (Grant No. 2015M572662XB). |
Corresponding Authors:
Jun He
E-mail: junhe@njnu.edu.cn
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Cite this article:
Han-Yu Jiang(姜寒玉) and Jun He(何军) Nonlinear signal transduction network with multistate 2021 Chin. Phys. B 30 118703
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[1] Kalckar H 1991 Ann. Rev. Biochem. 60 1 [2] Fang X, Kruse K, Lu T and Wang J 2019 Rev. Mod. Phys. 91 045004 [3] Braichenko S, Bhaskar A and Dasmahapatra S 2018 Phys. Rev. E 98 032413 [4] Hernandez-Hernandez G, Myers J, Alvarez-Lacalle E and Shiferaw Y 2017 Phys. Rev. E 95 032313 [5] Kholodenko B 2006 Nat. Rev. Mol. Cell. Biol. 7 165 [6] Barabási A and Oltvai Z 2004 Nat. Rev. Genet. 5 101 [7] Tong A H Y, Lesage G, Bader G D, Ding H and Xu H 2004 Science 303 808 [8] Albert R 2005 J. Cell. Sci. 118 4947 [9] Barabási A 2016 Network science (Cambridge: Cambridge University Press) [10] Rafo M, Mauro J and Aparicio J 2021 J. Theor. Biology 526 110554 [11] Wu Q and Hadzibeganovic T 2020 Appl. Math. Mod. 83 1 [12] Li L, Zhang J, Liu C, Zhang H, Wang Y and Wang Z 2019 Appl. Math. Comput. 347 566 [13] Viguerie A, Lorenzo G, Auricchio F, Baroli D, Hughes T, Patton A, Reali A, Yankeelov T and Veneziani A 2021 Appl. Math. Lett. 111 106617 [14] Wang Z, Andrews M, Wu Z, Wang L and Bauch C 2015 Phys. Life Rev. 15 1 [15] Li F, Long T, Lu Y, Ouyang Q and Tang C 2004 Proc. Nat. Acad. Sci. 101 4781 [16] Bers D 2000 Nature 415 198 [17] Berridge M, Bootman M and Lipp P 1998 Nature 395 645 [18] Gosak M, Markovič R, Dolenšek J, Rupnik M, Marhl M, Stožer A and Perc M 2018 Phys. Life Rev. 24 118 [19] Rothman J 1994 Nature 372 55 [20] Jing J, He L, Sun A, et al. 2015 Nat. Cell Biology 17 1339 [21] Prakriya M and Lewis R 2015 Physiol. Rev. 95 1383 [22] Jiang H Y and He J 2021 Commun. Theor. Phys. 73 015601 [23] Clapham D 2017 Cell 131 1047 [24] Berridge M 2016 Physiol. Rev. 96 1261 [25] Keizer J and Levine L 1996 Biophys. J. 71 3477 |
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