Abstract
Biomolecular pathways are built from diverse types of pairwise interactions, ranging from physical protein-protein interactions and modifications to indirect regulatory relationships. One goal of systems biology is to bridge three aspects of this complexity: the growing body of high-throughput data assaying these interactions; the specific interactions in which individual genes participate; and the genome-wide patterns of interactions in a system of interest. Here, we describe methodology for simultaneously predicting specific types of biomolecular interactions using high-throughput genomic data. This results in a comprehensive compendium of whole-genome networks for yeast, derived from ∼3,500 experimental conditions and describing 30 interaction types, which range from general (e.g. physical or regulatory) to specific (e.g. phosphorylation or transcriptional regulation). We used these networks to investigate molecular pathways in carbon metabolism and cellular transport, proposing a novel connection between glycogen breakdown and glucose utilization supported by recent publications. Additionally, 14 specific predicted interactions in DNA topological change and protein biosynthesis were experimentally validated. We analyzed the systems-level network features within all interactomes, verifying the presence of small-world properties and enrichment for recurring network motifs. This compendium of physical, synthetic, regulatory, and functional interaction networks has been made publicly available through an interactive web interface for investigators to utilize in future research at http://function.princeton.edu/bioweaver/.
Highlights
The complexity of cellular activity is driven by interactions among genes and gene products, and by the timing and dynamics of these interactions, the conditions under which they occur, and the many forms that they can take
The combination of high-throughput genomic data with advanced computational methods has enabled us to infer the first genomewide compendium of bimolecular pathway networks, comprising 30 distinct bimolecular interaction types. We demonstrate that this interaction network compendium, derived from,3,500 experimental conditions, can be used to direct a range of biomedical hypothesis generation and testing
We show that our results can be used to predict novel protein interactions and new pathway components, and that they enable system-level analysis to investigate the network characteristics of cell-wide regulatory circuits
Summary
The complexity of cellular activity is driven by interactions among genes and gene products, and by the timing and dynamics of these interactions, the conditions under which they occur, and the many forms that they can take. This ontology is hierarchical, in that a phosphate transfer is perforce a covalent post-translational modification, which is in turn by definition a transient physical interaction, and so forth We combine this ontology with Bayesian hierarchical classification methodology [6], enabling the simultaneous prediction of genome-wide interaction networks of all of these 30 types from integrated heterogeneous experimental data. We apply this method to a compendium of ,3,500 Saccharomyces cerevisiae experimental conditions, experimentally validating several of the resulting predictions in glucose utilization, DNA topological maintenance, and protein biosynthesis as described below. This methodology ensures that investigators can take advantage of all available data to accurately identify the entire range of functional interaction types within specific pathways and across an organism’s genome
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