Piyush Labhsetwar, John Andrew Cole, Elijah Roberts, Nathan D Price, and
Zaida A Luthey-Schulten.
Heterogeneity in protein expression induces metabolic variability in
a modeled Escherichiacoli population.
Proceedings of the National Academy of Sciences, USA,
110:14006-14011, 2013.
(PMC: PMC3752265)
LABH2013-ZLS
Stochastic gene expression can lead to phenotypic differences among cells even
in isogenic populations growing under macroscopically identical conditions.
Here, we apply flux balance analysis in investigating the effects of single-cell
proteomics data on the metabolic behavior of an in silico Escherichia coli
population. We use the latest metabolic reconstruction integrated with
transcriptional regulatory data to model realistic cells growing in a glucose
minimal medium under aerobic conditions. The modeled population exhibits a
broad distribution of growth rates, and principal component analysis was used to
identify well-defined subpopulations that differ in terms of their pathway use.
The cells differentiate into slow-growing acetate-secreting cells and fast-
growing CO2-secreting cells, and a large population growing at intermediate
rates shift from glycolysis to Entner-Doudoroff pathway use. Constraints
imposed by integrating regulatory data have a large impact on NADH oxidizing
pathway use within the cell. Finally, we find that stochasticity in the expression of
only a few genes may be sufficient to capture most of the metabolic variability of
the entire population.
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