Data coverage and model formulation reshape quantitative interpretations of bacterial transcriptional regulation

Thermodynamic models quantitatively describe interactions between transcription machinery and bacterial promoters. Contrary to conventional understanding, model analysis by Parisutham et al. (2025) attributes transcriptional inhibition by repressors to overstabilization of the RNA polymerase–promoter complex rather than prevention of its formation. Moreover, it suggests an inverse scaling relationship between basal promoter strength and transcriptional fold change, applicable to both repressor- and activator-mediated regulation. To reevaluate findings from this study, we systematically analyze empirical data and compare its framework with conventional thermodynamic models. In contrast to the inverse scaling relationship, data across multiple sources exhibit a peaked tradeoff between basal promoter strength and fold change, underscoring the importance of broad data coverage in revealing the full pattern required for reliable model inference. Furthermore, we identify the model assumption responsible for the apparent inverse scaling and misinterpretation of regulatory mechanisms. Relaxing this assumption enables the model to capture the peaked tradeoff and yield inferences consistent with established mechanisms of transcriptional repression and activation. We further derive a mathematical solution that connects basal expression to fold change for both repressor- and activator-regulated promoters. Our results underscore the importance of broad data coverage to avoid a “blind men and the elephant” interpretation and establish basal promoter strength as a key design parameter governing transcriptional regulation.