Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein∶protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein∶protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data.
Many cellular processes and the response of cells to environmental cues are determined by the intricate protein∶protein interactions. These cellular protein interactions can be represented in the form of a graph, where the nodes represent the proteins and the edges signify the interactions between them. However, the available protein functional linkage maps do not incorporate the dynamics of gene expression and thus do not portray the dynamics of true protein∶protein interactions in vivo. We have used gene expression data as well as the available protein functional interaction information for Escherichia coli to build the protein interaction networks for expressed genes in a given condition. These networks, named conditional networks, capture the differences in the protein interaction networks and hence the cell physiology. Thus, by exploring the dynamics of protein interaction profiles, we hope to understand the response of cells to environmental changes.
|ScienceOpen disciplines:||Quantitative & Systems biology|