Bioinformatics is a powerful tool to find genes associated with a function or involved in a regulatory network ( Figure 1B , left ) . In the case of paradigmatic organisms , such as E. coli , databases contain the up - to - date description of regulatory networks , including the organization of the genes in transcription units , operons , and entire regulons based on experimental data [ 17][18][19][20][21 ] . Additionally , curated databases containing exclusively functional information on TRs [ 22 ] or their putative DNA - binding sites [ 23][24][25 ] are constantly expanded . Hence , finding the annotated genes corresponding to a TR of interest linked to the regulated genes can be a direct task . In other bacteria , gene annotation is often not as complete ; thus , new regulatory networks must be established to find regulons of interest . There is a constant effort to improve regulon prediction with bioinformatics tools [ 26][27][28 ] to address the limits set by the degeneracy and variability of the conserved regulatory motifs in operons around their promoter regions . Thanks to this effort , the accuracy of bioinformatic algorithms that combine transcriptomic and other " -omics " data to their pipeline has greatly increased in the last decade [ 29,30 ] .