Figure 3.

Expanded investigations into the simulated outputs suggested more corrections to the input datasets. See also Figure S2. (A) Comparison of mean RNA copy numbers for mRNA genes that are part of polycistronic operons between simulations with and without operons in rich media conditions. Copy numbers were calculated by averaging the copy numbers from the first timesteps of simulated cells with fully completed cell cycles, excluding cells from the first two generations (n=743 for simulations without operons, n=731 for simulations with operons). Genes that have the top 5% largest values of $\left|t\right|$ $\left(\right|t|\ge 31.9)$, where $t$ is the t-statistic between the two distributions of RNA copy numbers, are highlighted in red. The shaded oval highlights genes whose RNA copy numbers were zero in simulations without operons, but nonzero in simulations with operons. (B) Schematic representation of how read counts are calculated in standard RNA-Seq protocols. (C) The transcription unit structure and the RNA-Seq read counts of the appCBXA operon, where the RNA-Seq read count of gene appX is reported as zero because of its short length. (D) Schematic representation of how the read counts of the appX mRNA were estimated from the transcription unit structure of the operon, and the RNA-Seq read counts of other genes in the operon. (E) Schematic representation of the manual alignment algorithm used to more correctly estimate the read counts of short genes. (F) Comparison of mean RNA-Seq read counts of 14 short genes in simulated cells, both simulated with (n=762) and without (n=768) operons, versus their read counts estimated from the manual alignment algorithm, averaged across multiple RNA samples (n=3). (G) Schematic representation of how adding a transcription unit spanning the stable genes of an operon could lead to a more accurate representation of the operon’s mRNA stoichiometries. (H) The distribution of the maximum values of $\left|t\right|$ among constituent genes for operons that had a transcription unit covering the stable genes in RegulonDB/EcoCyc (left), operons that did not have such a transcription unit (middle), and the same operons after adding the transcription unit covering the stable genes (right). (I) The transcription unit structures and the gene-level RNA-Seq read counts reported for the oppABCDF operon (top) and the Rend-seq data for the same operon³² (middle). Based on the existence of the 3’-end (arrow) downstream of gene oppA in the Rend-seq data, we added an additional transcription unit (oppA, red) to the operon (bottom). (J) The transcription unit structures and the gene-level RNA-Seq read counts reported for the cmk-rpsA-ihfB operon (top) and the Rend-seq data for the same operon³² (middle). Based on the existence of the 5’-ends and 3’-end (arrows) surrounding the gene rpsA in the Rend-seq data, we added an additional transcription unit (rpsA, red) to the operon (bottom).