Motivation: Global gene expression measurements as obtained, for example, in microarray experiments can provide important clues to the underlying transcriptional control mechanisms and network structure of a biological cell. In the absence of a detailed understanding of this gene regulation, current attempts at classification of expression data rely on clustering and pattern recognition techniques employing ad-hoc similarity criteria. To improve this situation, a better understanding of the expected relationships between expression profiles of genes associated by biological function is required. Results: It is shown that perturbation expansions familiar from biological systems theory make precise predictions for the types of relationships to be expected for expression profiles of biologically associated genes, even if the underlying biological factors responsible for this association are not known. Classification criteria are derived, most of which are not usually employed in clustering algorithms. The approach is illustrated by using the AtGenExpress Arabidopsis thaliana developmental expression map. ; Andreas W. Schreiber and Ute Baumann