Disentangling factors affecting bacterial transcriptional regulatory network inference
TL;DR
Using a 1035-sample E. coli expression database, two transcriptome principles are identified that support successful TRN inference, and dominantly regulated E. coli genes are identified, suggesting that simply regulated promoter subsets are central to effective TRN inference.
Abstract
Summary The rapid growth of bacterial gene expression databases has enabled computational inference of transcriptional regulatory networks (TRNs), yet it remains unclear why mathematically simple models often capture their apparent complexity. Using a 1035-sample E. coli expression database, we identify two transcriptome principles that support successful TRN inference. First, regulons defined from measured binding sites show limited overlap in gene membership, consistent with statistical independence exhibited by many successful inference methods. Second, 21% of genes, or 877 genes, exhibit regulator “dominance,” in which expression strongly correlates with a single regulator activity and receives minimal contributions from other regulators under most conditions. We formalize these properties with quantitative metrics and provide a reference catalog of dominantly regulated E. coli genes. Regulator dominance explains differences between expression-inferred and binding site-defined regulons, and removing dominated genes sharply reduces inference performance, suggesting that simply regulated promoter subsets are central to effective TRN inference.