# Quantitative Studies of Metabolic Switches in enteric bacteria

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $375,746

## Abstract

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.

## Key facts

- **NIH application ID:** 10241397
- **Project number:** 5R01GM109069-07
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** TERENCE HWA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $375,746
- **Award type:** 5
- **Project period:** 2014-02-17 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241397

## Citation

> US National Institutes of Health, RePORTER application 10241397, Quantitative Studies of Metabolic Switches in enteric bacteria (5R01GM109069-07). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10241397. Licensed CC0.

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