A major challenge in systems biology is to integrate our mechanistic understanding of gene regulation to predict quantitatively how cells will respond to environmental changes. Living cells respond rapidly to the availability of nutrients in part by altering production of ribosomal RNA (rRNA), a limiting component in the biosynthesis of ribosomes. Studies of rRNA transcription by the RNA polymerase of Escherichia coli have identified regulatory roles for guanosine tetraphosphate (ppGpp), the initiating nucleotide, and the protein DksA. To what extent findings from in vitro studies can be used to quantitatively predict in vivo responses to changing nutrient environments is unknown. We developed a mechanistic mathematical model for rRNA transcriptional responses to such changes. Our model accounts for binding of RNAP to its rRNA promoter to form a closed complex, isomerization from a closed complex to an open complex, reversible incorporation of the initiating NTP (iNTP), transcript elongation, and clearance of the promoter, Further, the model incorporates interactions between ppGpp and DksA with transcription intermediates, and it includes an empirical correction to account for salt effects. The model biophysical parameters were determined using 33 single- and multi-round transcription experiments spanning 487 in vitro measurements.' By incorporating in vivo measurements of ppGpp and ATP, the model correctly predicted rRNA production rates for cellular responses to nutrient upshifts, downshifts, and outgrowth into fresh medium. Inclusion of DksA was essential in all three cases. Our work provides a foundation for using data-driven computational models to predict the kinetics of in vivo transcriptional responses.