Sampled-Data State Estimation of Reaction Diffusion Genetic Regulatory Networks via Space-Dividing Approaches

Xiaona Song, Mi Wang, Shuai Song, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

A novel state estimator is designed for genetic regulatory networks with reaction-diffusion terms in this study. First, the diffusion space (where mRNA and protein exist) is divided into several parts and only a point, a line, or a plane, etc., is measured in every subspace to reduce the measurement cost effectively. Then, samplers and network-induced time delay are considered to meet the network transmission requirement. A new criterion to ensure that the estimation error converges to zero is established by using the Lyapunov functional combined with Wirtinger's inequality, reciprocally convex approach, and Halanay's inequality; furthermore, the estimator's parameters are derived by solving linear matrix inequalities. Finally, two simulation examples (including one-dimensional and two-dimensional spaces) are presented to demonstrate the developed scheme's applicability.

Original languageEnglish
Article number8723523
Pages (from-to)718-730
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume18
Issue number2
DOIs
Publication statusPublished - 2021 Mar 1

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant U1604146, in part by the Foundation for the University Technological Innovative Talents of Henan Province under Grant 18HASTIT019, in part by the National Research Foundation of Korea through the Ministry of Science, ICT and Future Planning under Grant NRF-2017R1A1A1A05001325, and in part by the Brain Korea 21 Plus Project in 2019.

Publisher Copyright:
© 2004-2012 IEEE.

Keywords

  • Data sampling
  • genetic regulatory networks
  • reaction-diffusion terms
  • space-dividing
  • state estimation

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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