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

8 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

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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