Covariance structure models are applied to gene expression data using a factor model, a path model, and their combination. The factor model is based on a few factors that capture most of the expression information. A common factor of a group of genes may represent a common protein factor for the transcript of the co-expressed genes, and hence, it has a biological interpretation. With simultaneous regressions among variables, a path analysis model is used to specify a structure based on biological pathways. Path models are applied to a simple cell cycle and the tricarboxylic acid cycle from yeast gene expression data. Finally, combining factor and path models, a covariance structure model produces a more complicated pathway structure of the yeast cell cycle involving genes and their underlying factors. All the models are estimated by maximum likelihood using the EQS software package.