isbscience.org/research/evaluating-effects-of-variable-single-cell-protein-expression-on-metabolism/Comment from Dr. Nathan Price: This PNAS paper evaluates effects of variable single-cell protein expression on metabolism. To my knowledge, it is the first integration of single cell proteomics data with a metabolic model. It shows that growth rate variability can be controlled by a small number of protein concentrations.
Title: Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population
Authors: Piyush Labhsetwara (a), John Andrew Cole (b), Elijah Roberts (c), Nathan D. Price (d), and Zaida A. Luthey-Schultena (a, b, c) [Affiliations: (a) Center for Biophysics and Computational Biology and Departments of Physics (b) and Chemistry (c), University of Illinois at Urbana–Champaign, Urbana, IL 61801; and (d) Institute for Systems Biology, Seattle, WA 98109]
Abstract: 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.
Link to paper in PNAS
