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.

Public Health Relevance

Global transcriptional regulation that is at the focus of the proposed study is predicted to coordinate transcription with changing translational capacity in different growth phases, resulting from, e.g., nutrient limitation, external stress or antibiotic treatment. Understanding how this global coordination helps bacteria to cope with harsh conditions and understanding how this coordination can be disrupted through interfering with the regulatory mechanisms could open up new perspective to enhance the efficacies of antibiotic treatment. Also, the ability to quantitatively connect transcript abundances (which are easy to observe) to protein concentrations (which drive cellular behaviors) will provide the research community with a precise way to diagnose problems and characterize bacterial responses, especially in stressful conditions where the coordination mechanisms may be broken.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Gaillard, Shawn R
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University of California, San Diego
Schools of Arts and Sciences
La Jolla
United States
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