Proteins combine in various ways to execute different essential functions. Cellular processes are enormously complex and it is a great challenge to explain the underlying organization. Various methods have been applied in attempt to reveal the organization of the cell. Gene expression analysis uses the mRNA levels in the cell to predict which proteins are present in the cell simultaneously. This method is useful but also known to sometimes fail. Proteins that are known to be functionally related do not always show a significant correlation in gene expression. This fact might be explained by the dynamic organization of the proteome. Proteins can have diverse functions and might interact with some proteins only during a few time points, which would probably not result in significant correlation in their gene expression. In this work we tried to address this problem by combining gene expression data with data for physical interactions between proteins. We used a method for modular decomposition introduced by Gagneur et al. (2004) that aims to reveal the logical organization in protein-protein networks. We extended the interpretation of the modular decomposition to localize the dynamics in the protein organization. We found evidence that protein-interactions supported by gene expression data are very likely to be related in function and thus can be used to predict function for unknown proteins. We also identified negative correlation in gene expression as an overlooked area. Several hypotheses were generated using combination of these methods. Some could be verified by the literature and others might shed light on new pathways after additional experimental testing.