ArticleProceedings of the National Academy of Sciences of the United States of America2013
Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed, 99 citations in OpenAlex.
- Flux sampling and context-specific genome-scale metabolic models for biotechnological applications.Trends in biotechnology · 2026Review
- Validated In Silico Population Model ofACS synthetic biology · 2022Article
- A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions.PNAS nexus · 2022Article
- Ecological modelling approaches for predicting emergent properties in microbial communities.Nature ecology & evolution · 2022Review
- Rifampicin Increases Expression of Plant Codon-Optimized Bacillus thuringiensis δ-Endotoxin Genes in Escherichia coli.The protein journal · 2022Article
- Multiple timescales in bacterial growth homeostasis.iScience · 2022Article
- Finding Liebig's law of the minimum.Ecological applications : a publication of the Ecological Society of America · 2021Article
- Monitoring single-cell dynamics of entry into quiescence during an unperturbed life cycle.eLife · 2021Article
- Microbial metabolic noise.WIREs mechanisms of disease · 2021Review
- Dynamic control in metabolic engineering: Theories, tools, and applications.Metabolic engineering · 2021Review
- Scalable, two-stage, autoinduction of recombinant protein expression in E. coli utilizing phosphate depletion.Biotechnology and bioengineering · 2020Article
- Simultaneous cross-evaluation of heterogeneousScience (New York, N.Y.) · 2020Article
- Evidence for rate-dependent filtering of global extrinsic noise by biochemical reactions in mammalian cells.Molecular systems biology · 2020Article
- Modeling population heterogeneity from microbial communities to immune response in cells.Cellular and molecular life sciences : CMLS · 2020Review
- Development and characterization of Escherichia coli triple reporter strains for investigation of population heterogeneity in bioprocesses.Microbial cell factories · 2020Article
- Computation of Single-Cell Metabolite Distributions Using Mixture Models.Frontiers in cell and developmental biology · 2020Article
- Pareto Optimality Explanation of the Glycolytic Alternatives in Nature.Scientific reports · 2019Article
- Essential metabolism for a minimal cell.eLife · 2019Article
- Stochastic modelling reveals mechanisms of metabolic heterogeneity.Communications biology · 2019Article
- Sources, propagation and consequences of stochasticity in cellular growth.Nature communications · 2018Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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