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Gene regulation is a complicated process. The interaction of many genes and their products forms an intricate biological network. Identification of this dynamic network will help us understand the biological processes in a systematic way. However, the construction of a dynamic network is very challenging for a high-dimensional system. In this article we propose to use a set of ordinary differential equations (ODE), coupled with dimensional reduction by clustering and mixed-effects modeling techniques, to model the dynamic gene regulatory network (GRN). The ODE models allow us to quantify both positive and negative gene regulation as well as feedback effects of genes in a functional module on the dynamic expression changes of genes in another functional module, which results in a directed graph network. A five-step procedure—clustering, smoothing, regulation identification, parameter estimates refining, and …