On the transient and steady-state estimates of interval genetic regulatory networks

Authors:
Ping Li;James Lam;Zhan Shu
Affiliations:
The Department of Mechanical Engineering, The University of Hong Kong, Hong Kong;The Department of Mechanical Engineering, The University of Hong Kong, Hong Kong;The Department of Mechanical Engineering, The University of Hong Kong, Hong Kong
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2010

Citing 7
Cited 5

A stabilization algorithm for a class of uncertain linear systems

Systems & Control Letters
Convex Optimization

Convex Optimization
Robust filtering for gene expression time series data with variance constraints

International Journal of Computer Mathematics - Bioinformatics
Global Robust Stability of Bidirectional Associative Memory Neural Networks With Multiple Time Delays

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust -Stabilization Design in Gene Networks Under Stochastic Molecular Noises: Fuzzy-Interpolation Approach

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New Delay-Dependent Exponential Stability for Neural Networks With Time Delay

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Exponential Stability Analysis for Neural Networks With Time-Varying Delay

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Robust state estimation for stochastic genetic regulatory networks

International Journal of Systems Science - Dynamics Analysis of Gene Regulatory Networks
A delay decomposition approach to fuzzy Markovian jumping genetic regulatory networks with time-varying delays

Fuzzy Sets and Systems
Robust filtering of extended stochastic genetic regulatory networks with parameter uncertainties, disturbances, and time-varying delays

Neurocomputing
Finite-time robust stochastic stability of uncertain stochastic delayed reaction-diffusion genetic regulatory networks

Neurocomputing
Robust state estimation for discrete-time stochastic genetic regulatory networks with probabilistic measurement delays

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is concernedwith the transient and steadystate estimates of a class of genetic regulatory networks (GRNs). Some sufficient conditions, which do not only present the transient estimate but also provide the estimates of decay rate and decay coefficient of the GRN with interval parameter uncertainties (interval GRN), are established by means of linear matrix inequality (LMI) and Lyapunov-Krasovskii functional.Moreover, the steady-state estimate of the proposed GRN model is also investigated. Furthermore, it is well known that gene regulation is an intrinsically noisy process due to intracellular and extracellular noise perturbations and environmental fluctuations. Then, by utilizing stochastic differential equation theory, the obtained results are extended to the case with noise perturbations due to natural random fluctuations. All the conditions are expressed within the framework of LMIs, which can easily be computed by using standard numerical software. A three-gene network is provided to illustrate the effectiveness of the theoretical results.