Time course studies of gene expression are essential in biomedical research to understand biological phenomena that evolve in a temporal fashion. Microarray technologies make it possible to study genome-wide temporal differences in gene expression profiles between different experimental conditions. In this paper, we introduce a functional hierarchical model for detecting emporally differentially expressed (TDE) genes between two experimental conditions for cross-sectional designs, where the gene expression profiles are treated as functional data and are modeled by basis function expansions. Monte Carlo EM algorithm is developed for estimating both the gene-specific parameters and the hyperparameters in the second level of the modeling. We use a direct posterior probability approach to bound the rate of false discovery at a pre-specified level. We evaluate the methods by simulations and application to a microarray time course gene expression data on C. elegans developmental processes. Simulation results suggested that the procedure performed better than the two-way ANOVA in identifying TDE genes, resulting in both higher sensitivity and specificity. Genes identified from the C. elegans developmental data set showed clear patterns of changes between the two experimental conditions.