title: Design of experiment for nonlinear dynamic gene regulatory network identification.
abstract: The gene regulatory network (GRN) is critical for understanding the regulatory interaction between genes. Time-course microarray experiments provide ample information for constructing GRN. The designs for microarray experiments serve different purposes. However, the experiment design specifically for GRN identification is still sparse. In this article, we use a simulation-based approach to deal with design problems in the framework of nonparametric differential equations. We investigate a number of feasible designs. In particular, we evaluate whether earlier samplings can result in more useful information for GRN identification. We also evaluate the effectiveness of two strategies: more frequent samplings per replicate with fewer replicates versus fewer samplings per replicate with more replicates while keeping the total number of samplings constant. The results of our investigation provide quantitative guidance for designing and selecting microarray experiments for the purpose of GRN identification.
authors.affiliation: a Department of Mathematics and Statistics , University of Nevada , Reno , Nevada , USA.
authors.forename: Tao
authors.initials: T
authors.lastname: Lu
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D017711:Nonlinear Dynamics; D012107:Research Design
keywords: Adaptive group LASSO; experiment design; mixed-effects models; time-course microarray data; variable selection