Recent improvements in high-throughput proteomics technology have produced a large amount of time-series gene expression data. The data provide a good resource to uncover causal gene-gene or gene-phenotype relationships and to characterize the dynamic properties of the underlying molecular networks for various biological processes. Several methods have been developed for identifying the molecular mechanisms of regulation of genes from the data, but many of the methods consider static gene expression profiles only. This paper presents a new method for identifying gene regulations from the time-series gene expression data and for visualizing the gene regulations as dynamic gene regulatory networks. The method has been implemented as a program called DRN Builder (Dynamic Regulatory Network Builder; http://wilab.inha.ac.kr/drnbuilder/) and successfully tested on actual gene expression profiles. DRN Builder will be useful for generating potential gene regulatory networks from a large amount of time-series gene expression data and for analyzing the identified networks.