The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
A universal trade-off between growth and lag in fluctuating environments
Open accessAbstract
The rate of cell growth is crucial for bacterial fitness and drives the allocation of bacterial resources, affecting, for example, the expression levels of proteins dedicated to metabolism and biosynthesis1,2. It is unclear, however, what ultimately determines growth rates in different environmental conditions. Moreover, increasing evidence suggests that other objectives are also important3–7, such as the rate of physiological adaptation to changing environments8,9. A common challenge for cells is that these objectives cannot be independently optimized, and maximizing one often reduces another. Many such trade-offs have indeed been hypothesized on the basis of qualitative correlative studies8–11. Here we report a trade-off between steady-state growth rate and physiological adaptability in Escherichia coli, observed when a growing culture is abruptly shifted from a preferred carbon source such as glucose to fermentation products such as acetate. These metabolic transitions, common for enteric bacteria, are often accompanied by multi-hour lags before growth resumes. Metabolomic analysis reveals that long lags result from the depletion of key metabolites that follows the sudden reversal in the central carbon flux owing to the imposed nutrient shifts. A model of sequential flux limitation not only explains the observed trade-off between growth and adaptability, but also allows quantitative predictions regarding the universal occurrence of such tradeoffs, based on the opposing enzyme requirements of glycolysis versus gluconeogenesis. We validate these predictions experimentally for many different nutrient shifts in E. coli, as well as for other respiro-fermentative microorganisms, including Bacillus subtilis and Saccharomyces cerevisiae. A model of sequential flux bottlenecks explains a universal trade-off between steady-state growth and physiological adaptation time in bacteria exposed to fluctuating growth conditions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products
Datsenko, K. A., & Wanner, B. L. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.120163297 · Cited by 14,782
Construction of Escherichia coli K‐12 in‐frame, single‐gene knockout mutants: the Keio collection
Baba, T., Ara, T., Hasegawa, M., Takai, Y., Okumura, Y., Baba, M., Datsenko, K. A., Tomita, M., Wanner, B. L., & Mori, H. (2006) · Molecular Systems Biology · doi:10.1038/msb4100050 · Cited by 7,858
A pyramid approach to subpixel registration based on intensity
Thevenaz, P., Ruttimann, U. E., & Unser, M. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.650848 · Cited by 3,078
A dynamic approach to predicting bacterial growth in food
Baranyi, J., & Roberts, T. A. (1994) · International Journal of Food Microbiology · doi:10.1016/0168-1605(94)90157-0 · Cited by 2,607
Interdependence of Cell Growth and Gene Expression: Origins and Consequences
Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z., & Hwa, T. (2010) · Science · doi:10.1126/science.1192588 · Cited by 1,470
Robust Growth of Escherichia coli
Wang, P., Robert, L., Pelletier, J., Dang, W. L., Taddei, F., Wright, A., & Jun, S. (2010) · Current Biology · doi:10.1016/j.cub.2010.04.045 · Cited by 1,014
Accurate quantitation of protein expression and site-specific phosphorylation
Oda, Y., Huang, K., Cross, F. R., Cowburn, D., & Chait, B. T. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.12.6591 · Cited by 1,006
Overflow metabolism in Escherichia coli results from efficient proteome allocation
Basan, M., Hui, S., Okano, H., Zhang, Z., Shen, Y., Williamson, J. R., & Hwa, T. (2015) · Nature · doi:10.1038/nature15765 · Cited by 785
Self-citationGrowth Rate-Dependent Global Effects on Gene Expression in Bacteria
Klumpp, S., Zhang, Z., & Hwa, T. (2009) · Cell · doi:10.1016/j.cell.2009.12.001 · Cited by 703
Evolutionary Trade-Offs, Pareto Optimality, and the Geometry of Phenotype Space
Shoval, O., Sheftel, H., Shinar, G., Hart, Y., Ramote, O., Mayo, A., Dekel, E., Kavanagh, K., & Alon, U. (2012) · Science · doi:10.1126/science.1217405 · Cited by 647
Cited by
A parallel glycolysis provides a selective advantage through rapid growth acceleration
Law, R. C., Nurwono, G., & Park, J. O. (2024) · Nature Chemical Biology · doi:10.1038/s41589-023-01395-2 · Cited by 30
The quantitative metabolome is shaped by abiotic constraints
Akbari, A., Yurkovich, J. T., Zielinski, D. C., & Palsson, B. O. (2021) · Nature Communications · doi:10.1038/s41467-021-23214-9 · Cited by 24
(2025) · doi:10.1128/msystems.00690-25 · Cited by 7
Hands-On Growth Laws Theory Cookbook
Droghetti, R., Corigliano, M., Calabrese, L., Fuchs, P., Vaidyanathan, A., Keisers, J., Micali, G., Lagomarsino, M. C., & Ciandrini, L. (2026) · PRX Life · doi:10.1103/x33x-srz4 · Cited by 5
Mechanistic insights into E. coli recovery from growth arrest
(2026) · doi:10.1038/s41467-026-71781-6 · Cited by 1
Metadata
- Authors
- Basan, M., Honda, T., Christodoulou, D., Hörl, M., Chang, Y.-F., Leoncini, E., Mukherjee, A., Okano, H., Taylor, B. R., Silverman, J. M., Sanchez, C., Williamson, J. R., Paulsson, J., Hwa, T., & Sauer, U.
- Venue
- Nature
- Published
- 2020-07-15
- Volume
- 584
- Issue
- 7821
- Pages
- 470-474
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3043338671
- Cited by
- 269
- References
- 50
- FWCI
- 8.57
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A universal trade-off between growth and lag in fluctuating environments. https://app.cochat.ai/papers/10.1038/s41586-020-2505-4
- The paper
- Basan, M., Honda, T., Christodoulou, D., Hörl, M., Chang, Y.-F., Leoncini, E., Mukherjee, A., Okano, H., Taylor, B. R., Silverman, J. M., Sanchez, C., Williamson, J. R., Paulsson, J., Hwa, T., & Sauer, U. (2020). A universal trade-off between growth and lag in fluctuating environments. Nature. https://doi.org/10.1038/s41586-020-2505-4
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.