| Summary |
Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism-environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis, we use new methods to integrate transcriptomics data with computational tools to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. To assess the accuracy of our model we tested 2030 predictions and confirmed 1485 regulatory interactions (36% of the full model), thus significantly increasing our understanding of various cell processes, such as spore formation.
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