The post-genomic era is marked by huge amounts of data generated by large-scale functional genomic and proteomic experiments. A major challenge is to integrate the various types of genome-scale information in order to reveal the intra- and inter- relationships between genes and proteins that constitute a living cell. Here we present a novel application of classical graph algorithms to integrate the cellular networks of protein-protein interactions and transcription regulation. We demonstrate how integration of these two networks enables the discovery of simple as well as complex regulatory circuits that involve both protein-protein and protein-DNA interactions. These circuits may serve for positive or negative feedback mechanisms. By applying our approach to data from the yeast Saccharomyces cerevisiae, we were able to identify known simple and complex regulatory circuits and to discover many putative circuits, whose biological relevance has been assessed using various types of experimental data. The newly identified relations provide new insight into the processes that take place in the cell, insight that could not be gained by analyzing each type of data independently. The computational scheme that we propose may be used to integrate additional functional genomic and proteomic data and to reveal other types of relations, in yeast as well as in higher organisms.