Intrinsic fluctuation and susceptibility in somatic cell reprogramming process
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摘要
Based on the coherent feedforward transcription regulation loops in somatic cell reprogramming process, a stochastic kinetic model is proposed to study the intrinsic fluctuations in the somatic cell reprogramming. The Fano factor formulas of key genes expression level in the coherent feedforward transcription regulation loops are derived by using of Langevin theory. It is found that the internal fluctuations of gene expression levels mainly depend on itself activation ratio and degradation ratio. When the self-activation ratio(or self-degradation ratio) is increased, the Fano factor increases reaches a maximum and then decreases. The susceptibility is used to measure the sensitivity of steady-state response to the variation in systemic parameters. It is found that with the increase of the self-activation ratio(or self-degradation ratio), the susceptibility of steady-state increases at first, it reaches a maximum, and it then decreases. The magnitude of the maximum is increased with the increase of activated ratio by the upstream transcription factor.
Based on the coherent feedforward transcription regulation loops in somatic cell reprogramming process, a stochastic kinetic model is proposed to study the intrinsic fluctuations in the somatic cell reprogramming. The Fano factor formulas of key genes expression level in the coherent feedforward transcription regulation loops are derived by using of Langevin theory. It is found that the internal fluctuations of gene expression levels mainly depend on itself activation ratio and degradation ratio. When the self-activation ratio(or self-degradation ratio) is increased, the Fano factor increases reaches a maximum and then decreases. The susceptibility is used to measure the sensitivity of steady-state response to the variation in systemic parameters. It is found that with the increase of the self-activation ratio(or self-degradation ratio), the susceptibility of steady-state increases at first, it reaches a maximum, and it then decreases. The magnitude of the maximum is increased with the increase of activated ratio by the upstream transcription factor.
Based on the coherent feedforward transcription regulation loops in somatic cell reprogramming process, a stochastic kinetic model is proposed to study the intrinsic fluctuations in the somatic cell reprogramming. The Fano factor formulas of key genes expression level in the coherent feedforward transcription regulation loops are derived by using of Langevin theory. It is found that the internal fluctuations of gene expression levels mainly depend on itself activation ratio and degradation ratio. When the self-activation ratio(or self-degradation ratio) is increased, the Fano factor increases reaches a maximum and then decreases. The susceptibility is used to measure the sensitivity of steady-state response to the variation in systemic parameters. It is found that with the increase of the self-activation ratio(or self-degradation ratio), the susceptibility of steady-state increases at first, it reaches a maximum, and it then decreases. The magnitude of the maximum is increased with the increase of activated ratio by the upstream transcription factor.
引文
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[2]Stadtfeld M,Maherali N,Breault D T and Hochedlinger K 2008 Cell Stem Cell 2 230
[3]Brambrink T,Foreman R,Welstead G G,Lengner C J,Wernig M,Suh H and Jaenisch R 2008 Cell Stem Cell 2 151
[4]Chan E M,Ratanasirintrawoot S,Park I H,Manos P D,Loh Y H,Huo H,Miller J D,Hartung O,Rho J,Ince T A,Daley G Q and Schlaeger TM 2009 Nat.Biotechnol.27 1033
[5]Stadtfeld M,Apostolou E,Akutsu H,Fukuda A,Follett P,Natesan S,Kono T,Shioda T and Hochedlinger K 2010 Nature 465 175
[6]Smith Z D,Nachman I,Regev A and Meissner A 2010 Nat.Biotechnol.28 521
[7]Mikkelsen T S,Hanna J,Zhang X,Ku M,Wernig M,Schorderet P,Bernstein B E,Jaenisch R,Lander E S and Meissner A 2008 Nature454 49
[8]Samavarchi-Tehrani P,Golipour A,David L,Sung H K,Beyer T A,Datti A,Woltjen K,Nagy A and Wrana J L 2010 Cell Stem Cell 7 64
[9]Hanna J,Saha K,Pando B,Zon J V,Lengner C J,Creyghton M P,Oudenaarden A V and Jaenisch R 2009 Nature 462 595
[10]Yamanaka S 2009 Nature 460 49
[11]Buganim Y,Faddah D A,Cheng A W,Itskovich E,Markoulaki S,Ganz K,Klemm S L,Oudenaarden A V and Jaenisch R 2012 Cell 150 1209
[12]Muraro M J,Kempe H and Verschuer P J 2013 Stem Cells 31 838
[13]MacArthur B D and Lemischka I R 2013 Cell 154 484
[14]Morrisa R,Sancho-Martinez I,Sharpee T O and Belmonte J C I 2014Proc.Natl.Acad.Sci.USA 111 5076
[15]Chung K M,Kolling I,Chung K M,F W I V,Gajdosik M D,Burger S,Russell A C and Nelson C E 2014 PLoS One 9 e95304
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[21]Zhang X P,Liu F,Cheng Z and Wang W 2009 Proc.Natl.Acad.Sci.USA 106 12245
[22]Wang D G,Zhou C H and Zhang X P 2017 Chin.Phys.B 26 128709
[23]Lu L L,Jia Y,Xu Y,Ge M Y,Yang L J and Zhan X 2019 Sci.China Technol.Sci.62 427
[24]Ge M,Jia Y,Xu Y and Yang L 2018 Nonlinear Dyn.91 515
[25]Xu Y,Jia Y,Ge M,Lu L,Yang L and Zhan X 2018 Neurocomputing283 196
[26]Ge M,Jia Y,Kirunda J B,Xu Y,Shen J,Lu L,Liu Y,Pei Q Zhan X and Yang L 2018 Neurocomputing 320 60
[27]Lu L,Jia Y,Kirunda J B,Xu Y,Ge M,Pei Q and Yang L 2018 Nonlinear Dyn.,November 11,pp.1-14
[28]Xu Y,Jia Y,Kirunda J B,Shen J,Ge M,Lu L and Pei Q 2018 Complexity 2018 3012743
[29]Lu L,Jia Y,Lu W and Yang L 2017 Complexity 2017 7628537
[30]Xu Y,Jia Y,Wang H,Liu Y,Wang P and Zhao Y 2019 Nonlinear Dyn.
[31]Alon U 2007 Nat.Rev.Genet.8 450
[32]Swain P S 2004 J.Mol.Biol.344 965
[33]Jia Y,Liu W,Li A,Yang L and Zhan X 2009 Biophys.Chem.143 80
[34]MacArthur B D,Please C P and Oreffo R O C 2008 PLoS One 3 e3086