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author:

Li, Y. (Li, Y..) [1] | Chen, J. (Chen, J..) [2] | Jiang, L. (Jiang, L..) [3] | Zeng, N. (Zeng, N..) [4] | Jiang, H. (Jiang, H..) [5] | Du, M. (Du, M..) [6]

Indexed by:

Scopus

Abstract:

The tumour suppressor gene p53 plays a key role in cell response to DNA damage, and the p53–Mdm2 regulation relationship is crucial for the expression of p53. In this paper, based on gene expression time-series data of human leukaemia cells after exposure to ionising radiation, with ionising radiation as input, a nonlinear continuous time-delay dynamic stochastic mathematical model of a p53–Mdm2 network is established using a continuous–discrete extended Kalman filter algorithm. The accuracy of the established model is then validated. Numerical simulation is used to simulate the dynamic regulation in p53–Mdm2 networks for low, medium, and high ionising radiation doses. The results show that the proposed algorithm is convergent and that the error rate of the model is only 1.19%. In addition, the model can simulate accurately the response of the p53–Mdm2 network to different doses of ionising radiation. The methods proposed in this paper can supply the foundation for research on the dynamics of the p53–Mdm2 gene regulation relationship and play a guiding role in research on the p53–Mdm2 response process after DNA damage under different doses of ionising radiation. © 2017 Elsevier B.V.

Keyword:

Continuous–discrete extended Kalman filter algorithm; p53–Mdm2 network; Time-series data Modelling

Community:

  • [ 1 ] [Li, Y.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, China
  • [ 2 ] [Li, Y.]Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, Fujian 350116, China
  • [ 3 ] [Chen, J.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, China
  • [ 4 ] [Chen, J.]Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, Fujian 350116, China
  • [ 5 ] [Jiang, L.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, China
  • [ 6 ] [Jiang, L.]Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, Fujian 350116, China
  • [ 7 ] [Zeng, N.]Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, Fujian 361005, China
  • [ 8 ] [Jiang, H.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, China
  • [ 9 ] [Jiang, H.]Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, Fujian 350116, China
  • [ 10 ] [Du, M.]Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, Fujian 350116, China
  • [ 11 ] [Du, M.]Fujian Key Lab of Eco-Industrial Green Technology, Wuyishan, Fujian 354300, China

Reprint 's Address:

  • [Li, Y.]College of Electrical Engineering and Automation, Fuzhou UniversityChina

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Source :

Neurocomputing

ISSN: 0925-2312

Year: 2018

Volume: 273

Page: 230-236

4 . 0 7 2

JCR@2018

5 . 5 0 0

JCR@2023

ESI HC Threshold:174

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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