Invariant nonequilibrium dynamics in gene regulation optimize information flow

Eukaryotic gene regulation relies on stochastic yet controlled promoter switching, in which genes transition between transcriptionally active and inactive states. Despite the molecular complexity of this process, recent studies have revealed a surprising invariance of the "switching correlation time" (T_C)-the characteristic decay time of the autocorrelation function of promoter activity fluctuations-across gene expression levels in multiple genes and organisms. A biophysically plausible explanation for this invariance has so far been lacking. Here, we show that this empirical constraint imposes stringent requirements on minimal yet realistic models of transcriptional regulation. Specifically, reproducing T_C-invariance requires regulatory architectures with at least four internal states and nonequilibrium dynamics that break detailed balance. Using Bayesian inference on Drosophila gap gene expression data, we demonstrate that such models i) quantitatively reproduce the observed T_C-invariance, ii) remain robust to parameter perturbations, and iii) maximize information transmission from transcription factor concentration to gene expression. Remarkably, the T_C-invariant modulation strategy we identify as optimal closely parallels contemporary control-theoretic results on the modulation of stochastic switching systems. Taken together, our results suggest that eukaryotic transcriptional regulation operates in a nonequilibrium regime to balance precision, reaction-rate limitations, and energy dissipation, thereby achieving near-optimal information transmission under fundamental physical constraints.