The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Mechanistic links between cellular trade-offs, gene expression, and growth
Open accessAbstract
Significance: Cells have finite resources. Committing resources to one task therefore reduces the amount of resources available to others. These trade-offs are often overlooked but potentially modify all cellular processes. Building a mathematical cell model that respects such trade-offs and describes the mechanisms of protein synthesis and how cells extract resources from their environment, we quantitatively recover the typical behavior of an individual growing cell and of a population of cells. As trade-offs are experienced by all cells and because growth largely determines cellular fitness, a predictive understanding of how biochemical processes affect others and affect growth is important for diverse applications, such as the use of microbes for biotechnology, the inhibition of antibiotic resistance, and the growth of cancers.
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Cites
Escherichia coli and Salmonella :cellular and molecular biology
Neidhardt, F. C. (1996) · ASM Press eBooks · Cited by 8,197
SMITH, J. M., & PRICE, G. R. (1973) · Nature · doi:10.1038/246015a0 · Cited by 5,089
A synthetic oscillatory network of transcriptional regulators
Elowitz, M. B., & Leibler, S. (2000) · Nature · doi:10.1038/35002125 · Cited by 4,630
THE GROWTH OF BACTERIAL CULTURES
Monod, J. (1949) · Annual Review of Microbiology · doi:10.1146/annurev.mi.03.100149.002103 · Cited by 3,707
Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells
Taniguchi, Y., Choi, P. J., Li, G.-W., Chen, H., Babu, M., Hearn, J., Emili, A., & Xie, X. S. (2010) · Science · doi:10.1126/science.1188308 · Cited by 1,982
The economics of ribosome biosynthesis in yeast
Warner, J. R. (1999) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(99)01460-7 · Cited by 1,933
Haario, H., Laine, M., Mira, A., & Saksman, E. (2006) · Statistics and Computing · doi:10.1007/s11222-006-9438-0 · Cited by 1,498
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
Schaechter, M., MaalOe, O., & Kjeldgaard, N. O. (1958) · Journal of General Microbiology · doi:10.1099/00221287-19-3-592 · Cited by 1,435
Evolution Experiments with Microorganisms: The Dynamics and Genetic Bases of Adaptation
Elena, S. F., & Lenski, R. E. (2003) · Nature Reviews Genetics · doi:10.1038/nrg1088 · Cited by 1,386
Cited by
Sources, propagation and consequences of stochasticity in cellular growth
Thomas, P., Terradot, G., Danos, V., & Weiße, A. Y. (2018) · Nature Communications · doi:10.1038/s41467-018-06912-9 · Cited by 107
Self-citationStart-Stop Assembly: a functionally scarless DNA assembly system optimized for metabolic engineering
Taylor, G. M., Mordaka, P. M., & Heap, J. T. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1182 · Cited by 91
An optimal regulation of fluxes dictates microbial growth in and out of steady state
Chure, G., & Cremer, J. (2023) · eLife · doi:10.7554/elife.84878 · Cited by 56
Mathematical properties of optimal fluxes in cellular reaction networks at balanced growth
Dourado, H., Liebermeister, W., Ebenhöh, O., & Lercher, M. J. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011156 · Cited by 14
Modeling host–pathway dynamics at the genome scale with machine learning
Merzbacher, C., Mac Aodha, O., & Oyarzún, D. A. (2025) · Metabolic Engineering · doi:10.1016/j.ymben.2025.05.008 · Cited by 13
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Metadata
- Authors
- Weiße, A. Y., Oyarzún, D. A., Danos, V., & Swain, P. S.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2015-02-18
- Volume
- 112
- Issue
- 9
- Pages
- E1038-47
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2076477598
- Cited by
- 403
- References
- 69
- FWCI
- 14.15
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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