The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
The Cost of Protein Production
Open accessAbstract
The economy of protein production is central to cell physiology, being intimately linked with cell division rate and cell size. Attempts to model cellular physiology are limited by the scarcity of experimental data defining the molecular processes limiting protein expression. Here, we distinguish the relative contribution of gene transcription and protein translation to the slower proliferation of budding yeast producing excess levels of unneeded proteins. In contrast to widely held assumptions, rapidly growing cells are not universally limited by ribosome content. Rather, transcription dominates cost under some conditions (e.g., low phosphate), translation in others (e.g., low nitrogen), and both in other conditions (e.g., rich media). Furthermore, cells adapted to enforced protein production by becoming larger and increasing their endogenous protein levels, suggesting limited competition for common resources. We propose that rapidly growing cells do not exhaust their resources to maximize growth but maintain sufficient reserves to accommodate changing requirements.
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Cites
Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants
Tong, A. H. Y., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. D., Pagé, N., Robinson, M., Raghibizadeh, S., Hogue, C. W. V., Bussey, H., et al. (2001) · Science · doi:10.1126/science.1065810 · Cited by 2,138
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
ENZYME INDUCTION AS AN ALL-OR-NONE PHENOMENON
Novick, A., & Weiner, M. (1957) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.43.7.553 · Cited by 891
Schuldiner, M., Collins, S. R., Thompson, N. J., Denic, V., Bhamidipati, A., Punna, T., Ihmels, J., Andrews, B., Boone, C., Greenblatt, J. F., et al. (2005) · Cell · doi:10.1016/j.cell.2005.08.031 · Cited by 870 · cited 2×
“To this end, we generated an additional library in which mCherry was expressed from a destabilized mRNA (DAmP [decreased abundance by mRNA perturbation]; Figure 4A) (Muhlrad and Parker, 1999; Schuldiner et al., 2005), thereby reducing the translation load while maintaining the same transcription burden.”
BackgroundMethodRate-Limiting Steps in Yeast Protein Translation
Shah, P., Ding, Y., Niemczyk, M., Kudla, G., & Plotkin, J. B. (2013) · Cell · doi:10.1016/j.cell.2013.05.049 · Cited by 463 · cited 1×
“While ribosome content could limit either initiation or elongation, a recent theoretical analysis concluded that initiation is the rate-limiting step in ribosome function (Shah et al., 2013).”
BackgroundEmergence of robust growth laws from optimal regulation of ribosome synthesis
Scott, M., Klumpp, S., Mateescu, E. M., & Hwa, T. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145379 · Cited by 443
Invariant Distribution of Promoter Activities in Escherichia coli
Zaslaver, A., Kaplan, S., Bren, A., Jinich, A., Mayo, A., Dekel, E., Alon, U., & Itzkovitz, S. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000545 · Cited by 92
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Nielsen, J. (2017) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061516-044757 · Cited by 252
Central dogma rates and the trade-off between precision and economy in gene expression
Hausser, J., Mayo, A., Keren, L., & Alon, U. (2019) · Nature Communications · doi:10.1038/s41467-018-07391-8 · Cited by 188
Elsemman, I. E., Rodriguez Prado, A., Grigaitis, P., Garcia Albornoz, M., Harman, V., Holman, S. W., van Heerden, J., Bruggeman, F. J., Bisschops, M. M. M., Sonnenschein, N., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28467-6 · Cited by 120
Systems Biology of Lactic Acid Bacteria: For Food and Thought
Teusink, B., & Molenaar, D. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.07.005 · Cited by 95
Davis, L., Recktenwald, M., Hutt, E., Fuller, S., Briggs, M., Goel, A., & Daringer, N. (2022) · Cancers · doi:10.3390/cancers14051259 · Cited by 83
Lessons on enzyme kinetics from quantitative proteomics
Davidi, D., & Milo, R. (2017) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.02.007 · Cited by 72
Kafri, M., Metzl-Raz, E., Jonas, F., & Barkai, N. (2016) · FEMS Yeast Research · doi:10.1093/femsyr/fow081 · Cited by 48
Self-citationHomeostasis in the Central Dogma of molecular biology: the importance of mRNA instability
Pérez-Ortín, J. E., Tordera, V., & Chávez, S. (2019) · RNA Biology · doi:10.1080/15476286.2019.1655352 · Cited by 35
Resource allocation in mammalian systems
Baghdassarian, H. M., & Lewis, N. E. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108305 · Cited by 17
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
Noise robustness and metabolic load determine the principles of central dogma regulation
Lo, T. W., Choi, H. J., Huang, D., & Wiggins, P. A. (2024) · Science Advances · doi:10.1126/sciadv.ado3095 · Cited by 13
(2025) · doi:10.1128/msystems.00690-25 · Cited by 7
Thermodynamics shape the in vivo enzyme burden of glycolytic pathways
Khana, D. B., Jen, A., Shishkova, E., Thusoo, E., Williams, J., Henkel, A., Stevenson, D. M., Coon, J. J., & Amador-Noguez, D. (2025) · doi:10.1101/2025.01.31.635972 · Cited by 5
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
Homeostasis in the Central Dogma of Molecular Biology: the importance of mRNA instability
Pérez-Ortín, J. E., Tordera, V., & Chávez, S. (2019) · doi:10.1101/599050 · Cited by 1
Nearly maximal information gain due to time integration in central dogma reactions
Sarkar, S., & Rammohan, J. (2023) · iScience · doi:10.1016/j.isci.2023.106767
Metadata
- Authors
- Kafri, M., Metzl-Raz, E., Jona, G., & Barkai, N.
- Venue
- Cell Reports
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 14
- Issue
- 1
- Pages
- 22-31
- ISSN
- 2211-1247 (ISSN-L)
- OpenAlex
- W2219121760
- Cited by
- 355
- References
- 7
- FWCI
- 9.91
- Type
- journal article
- Language
- en
- Open access
- gold
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