Project Summary Attaining quantitative, predictive understanding of cellular behaviors from the knowledge of molecular parts and interactions is one of the foremost challenges of systems biology. In the previous grant period, we established a kinetic model to predict the proteome dynamics in the model bacterium E. coli, in response to changing environmental conditions. In this next grant period, we propose to extend this work to predicting the dynamics of the transcriptome. This is a much more challenging task than predicting the proteome dynamics, because unlike the proteome, even the steady- state characteristics of the transcriptome have not been understood at a quantitative level; in particular the link between the transcriptome and proteome is poorly understood. Our preliminary data identified a previously unknown global transcriptional regulation in E. coli as the missing link. We propose to establish this global regulatory effect quantitatively in different growth conditions, and to elucidate the molecular mechanism and strategy underlying this regulation. We will validate and exploit the predicted coordination between transcriptional and translational capacities provided by this global regulation to establish quantitative links between transcriptional regulation and cellular mRNA and protein levels for many genes in E. coli. By incorporating the knowledge on transcriptional regulation into the kinetic model of proteome dynamics developed so far, we will establish a framework to predict the dynamics of the transcriptome during growth transitions. Experimental components of this research involve a combination of modern ‘omic methodologies and classical biochemical analysis. Specifically, RNA-seq data will be collected for a broad range of growth conditions (various types of nutrient limitations, antibiotic treatment, transient shifts) and for strains with different genetic backgrounds including titratable mutants. The RNA-seq data will be further complemented by the absolute determination of total mRNA abundances and fluxes to enable comparison across conditions. The data will then be integrated with quantitative proteomic and metabolomic data we have already collected across the same growth conditions, so that they can be related to cellular physiology and enable quantitative analysis and model building. The latter will combine the unique experiences available at the PI’s lab, involving detailed quantitative modeling of transcriptional and post-transcriptional regulation for specific genes and mRNAs on the one hand, and coarse-grained modeling of genome-scale dynamics on the other hand.