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"This book offers an essential introduction to the latest advances in delayed genetic regulatory networks (GRNs) and presents cutting-edge work on the analysis and design of delayed GRNs in which the system parameters are subject to uncertain, stochastic and/or parameter-varying changes. Specifically, the types examined include delayed switching GRNs, delayed stochastic GRNs, delayed reaction-diffusion GRNs, delayed discrete-time GRNs, etc. In addition, the solvability of stability analysis, control and estimation problems involving delayed GRNs are addressed in terms of linear matrix inequality or M-matrix tests."

"ABSTRAKThis paper proposes methods for forward and inverse system modeling using Bayesian and least squares regression. This methods are based on both space-filling design criteria for multiple response problems and linear optimality criteria focusing on D optimality. Modeling with and without the constant term is considered motivated by the case study application of genetic network modeling. We propose extended one-factor-at-a-time experimentation followed bye augmentation of next stage design which offers biologists simplicity. Result are illustrated both numerical examples, a test problem from the literature, and a case study motivated by an real world biological research related to genetic network modeling."