Functionality Encoded In Topology? Discovering Macroscopic Regulatory Modules from Large-Scale Protein-DNA Interaction Networks

Abstract

The promise of discovering a functional blueprint of a cellular system from large-scale and high-throughput sequence and experimental data is predicated on the belief that the same top-down investigative approach that proved successful in other biological problems (e.g. DNA sequencing) will be as effective when it comes to inferring more complex intracellular processes. The results in this paper address this fundamental issue in the specific context of transcription regulatory networks. Although simple recurring regulatory motifs have been identified in the past, due to the size and complexity of the connectivity structure, the subdivision of such networks into larger, and possibly inter-connected, regulatory modules is still under investigation. Specifically, it is unclear whether functionally well-characterized transcriptional sub-networks can be identified by solely analyzing the connectivity structure of the overall network topology. In this paper, we show that transcriptional regulatory networks can be systematically partitioned into communities whose members are consistently functionally related. We applied the partitioning method to the transcriptional regulatory networks of the yeast Saccharomyces cerevisiae; the resulting communities of gene and transcriptional regulators can be associated to distinct functional units, such as amino acid metabolism, cell cycle regulation, protein biosynthesis and localization, DNA replication and maintenance, lipid catabolism, stress response and so on. Moreover, the observation of inter-community connectivity patterns provides a valuable tool for elucidating the inter-dependency between the discovered regulatory modules.