A moments-based approach for inferring mechanisms of transcriptional regulation using nascent RNA data

Abstract Unraveling the kinetics of transcriptional regulation is a central challenge in molecular biology. Traditional inference methods based on cell-to-cell mRNA variability are limited because mRNA levels reflect not only transcriptional activity but also post-transcriptional processes. Recent advances in quantifying nascent RNAs across isogenic cells provide a more direct readout of transcriptional dynamics. Here, we present a moments-based theoretical framework that leverages nascent RNA data to infer transcriptional kinetics. We derive closed-form expressions for the first two time-dependent moments of nascent RNA across three canonical transcription models and demonstrate that nascent RNA exhibits distinct transient overshoots and higher Fano factors compared to mature mRNA. Through analysis of synthetic datasets, we show that these features enable more accurate estimation of transcriptional kinetics and allow reliable model selection, distinguishing single from multi-pathway promoter activation with over 75% accuracy even from small sample sizes. We further validated our model selection method using real experimental transcription datasets in yeast and E. coli cells, with nascent RNA demonstrating superior performance in identifying cross-talk pathway regulation. Our framework thus offers a computationally efficient and mechanistically informative approach for decoding transcriptional dynamics, emphasizing the advantages of time-dependent nascent RNA measurements over mature mRNA in capturing the true kinetics of transcriptional regulation.