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Growth Rate-Dependent Global Effects on Gene Expression in Bacteria
Open accessAbstract
Bacterial gene expression depends not only on specific regulatory mechanisms, but also on bacterial growth, because important global parameters such as the abundance of RNA polymerases and ribosomes are all growth-rate dependent. Understanding of these global effects is necessary for a quantitative understanding of gene regulation and for the design of synthetic genetic circuits. We find that the observed growth-rate dependence of constitutive gene expression can be explained by a simple model using the measured growth-rate dependence of the relevant cellular parameters. More complex growth dependencies for genetic circuits involving activators, repressors, and feedback control were analyzed and verified experimentally with synthetic circuits. Additional results suggest a feedback mechanism mediated by general growth-dependent effects that does not require explicit gene regulation if the expressed protein affects cell growth. This mechanism can lead to growth bistability and promote the acquisition of important physiological functions such as antibiotic resistance and tolerance (persistence).
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“In the simplest case, the total proteome is partitioned into a growth rate-independent fraction that may include negatively autoregulated housekeeping genes (Klumpp et al, 2009), and growth rate-dependent fractions, one for ribosomal and other translational proteins, and one for metabolic proteins, including transporters and catabolic and anabolic enzymes (Scott et al, 2010; Scott & Hwa, 2011).”
From Emergence of robust growth laws from optimal regulation of ribosome synthesis (2014) · cites this paper as Klumpp et al, 2009
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“Naively, one might expect modularity of cellular organization to insulate typical genetic circuits from the physiology of cell growth. This does not appear to be the case (Klumpp et al, 2009)”
From Emergence of robust growth laws from optimal regulation of ribosome synthesis (2014) · cites this paper as Klumpp et al, 2009
In the citing paper
“In fact, the bottom-up approach cannot even predict the growth rate dependence of gene expression from an unregulated (or “constitutive”) promoter, which is often taken as the reference state in the study of gene regulatory processes [see (Klumpp et al, 2009) for more discussion of the shortcomings of the bottom-up approach].”
From Emergence of robust growth laws from optimal regulation of ribosome synthesis (2014) · cites this paper as Klumpp et al, 2009
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Experiments in molecular genetics
Miller, J. H. (1972) · Medical Entomology and Zoology · Cited by 27,157
One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products.
Datsenko, K., & Wanner, B. (2000) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.120163297 · Cited by 15,902
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441
A synthetic oscillatory network of transcriptional regulators
Elowitz, M., & Leibler, S. (2000) · Nature · doi:10.1038/35002125 · Cited by 4,812
Construction of a genetic toggle switch in Escherichia coli
Gardner, T. S., Cantor, C. R., & Collins, J. J. (2000) · Nature · doi:10.1038/35002131 · Cited by 4,310
Network motifs in the transcriptional regulation network of Escherichia coli
Shen-Orr, S. S., Milo, R., Mangan, S., & Alon, U. S. (2002) · Nature Genetics · doi:10.1038/ng881 · Cited by 2,970
Bacterial Persistence as a Phenotypic Switch
Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L., & Leibler, S. (2004) · Science · doi:10.1126/science.1099390 · Cited by 2,952
Culture Medium for Enterobacteria
Neidhardt, F. C., Bloch, P. L., & Smith, D. F. (1974) · Journal of Bacteriology · doi:10.1128/jb.119.3.736-747.1974 · Cited by 2,903
Lutz, R. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.6.1203 · Cited by 1,694
Molecular mechanisms that confer antibacterial drug resistance
Walsh, C. T. (2000) · Nature · doi:10.1038/35021219 · Cited by 1,579
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Research works printed but not linked: 1
- Willumsen, B.M. (1975). Expression of constitutive genes in E. coli as function of growth rate (M. Sc. Thesis, University of Copenhagen).
Cited by
Sorted by citations
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
Self-citationCellular Decision Making and Biological Noise: From Microbes to Mammals
Balázsi, G., van Oudenaarden, A., & Collins, J. J. (2011) · Cell · doi:10.1016/j.cell.2011.01.030 · Cited by 1,017
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-citationThe quantitative and condition-dependent Escherichia coli proteome
Schmidt, A., Kochanowski, K., Vedelaar, S. R., Ahrné, E., Volkmer, B., Callipo, L., Knoops, K., Bauer, M., Aebersold, R. H., & Heinemann, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3418 · Cited by 759
Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models
Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., Charusanti, P., Polpitiya, A. D., Adkins, J., Schramm, G., Purvine, S. O., López‐Ferrer, D., et al. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.47 · Cited by 748
Quantifying cellular capacity identifies gene expression designs with reduced burden
Ceroni, F., Algar, R., Stan, G.-B. V., & Ellis, T. (2015) · Nature Methods · doi:10.1038/nmeth.3339 · Cited by 491
Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction
O'Brien, E. J., Lerman, J. A., Chang, R. L., Hyduke, D. R., & Palsson, B. Ø. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.52 · Cited by 471
Sequential Establishment of Stripe Patterns in an Expanding Cell Population
Liu, C., Fu, X., Liu, L., Ren, X., Chau, C. K., Li, S., Lu, X., Zeng, H., Chen, G., Tang, L., et al. (2011) · Science · doi:10.1126/science.1209042 · Cited by 459
Cardinale, S., & Arkin, A. P. (2012) · Biotechnology Journal · doi:10.1002/biot.201200085 · Cited by 448
Emergence 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
Self-citationCoordination of bacterial proteome with metabolism by cyclic AMP signalling
You, C., Okano, H., Hui, S., Zhang, Z., Kim, M., Gunderson, C. W., Wang, Y.-P., Lenz, P., Yan, D., & Hwa, T. (2013) · Nature · doi:10.1038/nature12446 · Cited by 432
Self-citationEngineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology
Wang, B., Kitney, R. I., Joly, N., & Buck, M. (2011) · Nature Communications · doi:10.1038/ncomms1516 · Cited by 423
Stochasticity of metabolism and growth at the single-cell level
Kiviet, D. J., Nghe, P., Walker, N., Boulineau, S., Šunderlíková, V., & Tans, S. J. (2014) · Nature · doi:10.1038/nature13582 · Cited by 419
Visual account of protein investment in cellular functions
Liebermeister, W., Noor, Ε., Flamholz, A. I., Davidi, D., Bernhardt, J., & Milo, R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314810111 · Cited by 419
General calibration of microbial growth in microplate readers
Stevenson, K., McVey, A. F., Clark, I. B. N., Swain, P. S., & Pilizota, T. (2016) · Scientific Reports · doi:10.1038/srep38828 · Cited by 407
Mechanistic links between cellular trade-offs, gene expression, and growth
Weiße, A. Y., Oyarzún, D. A., Danos, V., & Swain, P. S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1416533112 · Cited by 403
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Hui, S., Silverman, J. M., Chen, S. S., Erickson, D. W., Basan, M., Wang, J., Hwa, T., & Williamson, J. R. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145697 · Cited by 369
Self-citationRegulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence
Rotem, E., Loinger, A., Ronin, I., Levin-Reisman, I., Gabay, C., Noam Shoresh, F., Biham, O., & Balaban, N. Q. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1004333107 · Cited by 351
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Shamsabadi, A. A., Gh., M. S., Anasori, B., & Soroush, M. (2018) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.8b03823 · Cited by 343
Bacterial growth laws and their applications
Scott, M., & Hwa, T. (2011) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2011.04.014 · Cited by 309
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Klumpp, S., Scott, M., Pedersen, S., & Hwa, T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310377110 · Cited by 293
Self-citationMetabolic division of labor in microbial systems
Tsoi, R., Wu, F., Zhang, C., Bewick, S. A., Karig, D., & You, L. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1716888115 · Cited by 301
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Tramontano, M., Andrejev, S., Pruteanu, M., Klünemann, M., Kuhn, M., Galardini, M., Jouhten, P., Zelezniak, A., Zeller, G., Bork, P., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0123-9 · Cited by 295
A universal trade-off between growth and lag in fluctuating environments
Basan, M., Honda, T., Christodoulou, D., Hörl, M., Chang, Y., Leoncini, E., Mukherjee, A., Okano, H., Taylor, B. R., Silverman, J. M., et al. (2020) · Nature · doi:10.1038/s41586-020-2505-4 · Cited by 263
Resource Competition Shapes the Response of Genetic Circuits
Qian, Y., Huang, H.-H., Jiménez, J. I., & Vecchio, D. D. (2017) · ACS Synthetic Biology · doi:10.1021/acssynbio.6b00361 · Cited by 251
Isocost Lines Describe the Cellular Economy of Genetic Circuits
György, A., Jiménez, J. I., Yazbek, J., Huang, H.-H., Chung, H., Weiss, R., & Vecchio, D. D. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2015.06.034 · Cited by 248
Overloaded and stressed: whole-cell considerations for bacterial synthetic biology
Borkowski, O., Ceroni, F., Stan, G.-B. V., & Ellis, T. (2016) · Current Opinion in Microbiology · doi:10.1016/j.mib.2016.07.009 · Cited by 249
Invariance of Initiation Mass and Predictability of Cell Size in Escherichia coli
Si, F., Li, D., Cox, S. E., Sauls, J. T., Azizi, O., Sou, C., Schwartz, A. B., Erickstad, M. J., Jun, Y., Li, X., et al. (2017) · Current Biology · doi:10.1016/j.cub.2017.03.022 · Cited by 243
Bacterial growth: global effects on gene expression, growth feedback and proteome partition
Klumpp, S., & Hwa, T. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.01.001 · Cited by 218
Self-citationPromoter architecture dictates cell-to-cell variability in gene expression
Jones, D., Brewster, R. C., & Phillips, R. (2014) · Science · doi:10.1126/science.1255301 · Cited by 223
Engineering metabolism through dynamic control
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Functioning of a metabolic flux sensor in Escherichia coli
Kochanowski, K., Volkmer, B., Gerosa, L., Haverkorn van Rijsewijk, B. R., Schmidt, A., & Heinemann, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1202582110 · Cited by 196
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Mutalik, V. K., Guimaraes, J. C., Cambray, G., Mai, Q.-A., Christoffersen, M. J., Martin, L., Yu, A., Lam, C. S. C., Rodríguez, C., Bennett, G., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2403 · Cited by 199
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Keren, L., Zackay, O., Lotan‐Pompan, M., Barenholz, U., Dekel, E., Sasson, V., Aidelberg, G., Bren, A., Zeevi, D., Weinberger, A., et al. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.59 · Cited by 198
The Innate Growth Bistability and Fitness Landscapes of Antibiotic-Resistant Bacteria
Deris, J. B., Kim, M., Zhang, Z., Okano, H., Hermsen, R., Groisman, A., & Terence Hwa, B. (2013) · Science · doi:10.1126/science.1237435 · Cited by 193
Self-citationJun, S., Si, F., Pugatch, R., & Scott, M. (2018) · Reports on Progress in Physics · doi:10.1088/1361-6633/aaa628 · Cited by 193
Chubukov, V., Uhr, M., Le Chat, L., Kleijn, R. J., Jules, M., Link, H., Aymerich, S., Stelling, J., & Sauer, U. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.66 · Cited by 191
On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient
Hermsen, R., Deris, J. B., & Terence Hwa, B. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117716109 · Cited by 186
Bacterial persistence is an active σS stress response to metabolic flux limitation
Radzikowski, J. L., Vedelaar, S. R., Siegel, D. J., Ortega, A. D., Schmidt, A., & Heinemann, M. (2016) · Molecular Systems Biology · doi:10.15252/msb.20166998 · Cited by 186
A ‘resource allocator’ for transcription based on a highly fragmented T7 RNA polymerase
Segall-Shapiro, T. H., Meyer, A. J., Ellington, A. D., Sontag, E. D., & Voigt, C. A. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145299 · Cited by 182
Harke, M. J., & Gobler, C. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069834 · Cited by 180
Engineered promoters enable constant gene expression at any copy number in bacteria
Segall-Shapiro, T. H., Sontag, E. D., & Voigt, C. A. (2018) · Nature Biotechnology · doi:10.1038/nbt.4111 · Cited by 172
de Groot, D. H., van Boxtel, C., Planqué, R., Bruggeman, F. J., & Teusink, B. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006858 · Cited by 172
Growth‐dependent bacterial susceptibility to ribosome‐targeting antibiotics
Greulich, P., Scott, M., Evans, M. R., & Allen, R. J. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145949 · Cited by 152
Bacterial growth laws reflect the evolutionary importance of energy efficiency
Maitra, A., & Dill, K. A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421138111 · Cited by 166
Evolutionary adaptations to new environments generally reverse plastic phenotypic changes
Ho, W.-C., & Zhang, J. (2018) · Nature Communications · doi:10.1038/s41467-017-02724-5 · Cited by 156
Ryall, B., Eydallin, G., & Ferenci, T. (2012) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.05028-11 · Cited by 160
Gerosa, L., van Rijsewijk, B. R. B. H., Christodoulou, D., Kochanowski, K., Schmidt, T., Noor, Ε., & Sauer, U. (2015) · Cell Systems · doi:10.1016/j.cels.2015.09.008 · Cited by 161
Persistence: mechanisms for triggering and enhancing phenotypic variability
Balaban, N. (2011) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2011.10.001 · Cited by 156
Noise-driven growth rate gain in clonal cellular populations
Hashimoto, M., Nozoe, T., Nakaoka, H., Okura, R., Akiyoshi, S., Kaneko, K., Kussell, E. L., & Wakamoto, Y. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1519412113 · Cited by 149
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Self-citationBaumschlager, A., Aoki, S. K., & Khammash, M. (2017) · ACS Synthetic Biology · doi:10.1021/acssynbio.7b00169 · Cited by 153
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Firouzjaei, M. D., Shamsabadi, A. A., Gh., M. S., Rahimpour, A., & Soroush, M. (2018) · Advanced Materials Interfaces · doi:10.1002/admi.201701365 · Cited by 143
Kim, M., Rai, N., Zorraquino, V., & Tagkopoulos, I. (2016) · Nature Communications · doi:10.1038/ncomms13090 · Cited by 141
Global landscape of cell envelope protein complexes in Escherichia coli
Babu, M., Bundalovic-Torma, C., Calmettes, C., Phanse, S., Zhang, Q., Jiang, Y., Minić, Z., Kim, S., Mehla, J., Gagarinova, A. G., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.4024 · Cited by 130
Self-citationBerthoumieux, S., de Jong, H., Baptist, G., Pinel, C., Ranquet, C., Ropers, D., & Geiselmann, J. (2013) · Molecular Systems Biology · doi:10.1038/msb.2012.70 · Cited by 134
Few regulatory metabolites coordinate expression of central metabolic genes in Escherichia coli
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Predictable tuning of protein expression in bacteria
Bonde, M., Pedersen, M., Klausen, M. S., Jensen, S. I., Wulff, T., Harrison, S. J., Nielsen, A. T., Herrgård, M. J., & Sommer, M. O. A. (2016) · Nature Methods · doi:10.1038/nmeth.3727 · Cited by 133
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Long, Z., Nugent, E., Javer, A., Cicuta, P., Sclavi, B., Lagomarsino, M. C., & Dorfman, K. D. (2013) · Lab on a Chip · doi:10.1039/c2lc41196b · Cited by 128
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Egli, T. W. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.00287 · Cited by 124
Fasani, R. A., & Savageau, M. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1301023110 · Cited by 119
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Payne, S., Li, B., Cao, Y., Schaeffer, D. G., Ryser, M. D., & You, L. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.55 · Cited by 119
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Sandoval‐Motta, S., & Aldana, M. (2016) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1335 · Cited by 116
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Warren, M. R., Sun, H., Yan, Y., Cremer, J., Ли, Б., & Hwa, T. (2019) · eLife · doi:10.7554/elife.41093 · Cited by 110
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Zavřel, T., Faizi, M., Loureiro, C., Poschmann, G., Stühler, K., Sinetova, M. A., Zorina, A. A., Steuer, R., & Červený, J. (2019) · eLife · doi:10.7554/elife.42508 · Cited by 117
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Qian, Y., & Vecchio, D. D. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0902 · Cited by 112
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Thomas, P., Terradot, G., Danos, V., & Weiße, A. Y. (2018) · Nature Communications · doi:10.1038/s41467-018-06912-9 · Cited by 107
Boo, A., Ellis, T., & Stan, G.-B. V. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.03.001 · Cited by 106
Harvey, P., Watson, M., Hulme, S. D., Jones, M. A., Lovell, M., Berchieri, Â., Young, J. R., Bumstead, N., & Barrow, P. (2011) · Infection and Immunity · doi:10.1128/iai.01390-10 · Cited by 105
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Liao, C., Blanchard, A. E., & Lu, T. (2017) · Nature Microbiology · doi:10.1038/s41564-017-0022-5 · Cited by 100
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Sabri, S., Steen, J. A., Bongers, M., Nielsen, L. K., & Vickers, C. E. (2013) · Microbial Cell Factories · doi:10.1186/1475-2859-12-60 · Cited by 96
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Waldherr, S., Oyarzún, D. A., & Bockmayr, A. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.10.035 · Cited by 102
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Scholes, N. S., Schnoerr, D., Isalan, M., & Stumpf, M. P. H. (2019) · Cell Systems · doi:10.1016/j.cels.2019.07.007 · Cited by 95
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Noise in gene expression is coupled to growth ratePreprint
Keren, L., van Dijk, D., Weingarten-Gabbay, S., Davidi, D., Jona, G., Weinberger, A., Milo, R., & Segal, E. (2015) · Genome Research · doi:10.1101/gr.191635.115 · Cited by 89
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Arpino, J. A. J., Hancock, E. J., Anderson, J., Barahona, M., Stan, G.-B. V., Papachristodoulou, A., & Polizzi, K. M. (2013) · Microbiology · doi:10.1099/mic.0.067975-0 · Cited by 92
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Bienick, M. S., Young, K. W., Klesmith, J. R., Wrenbeck, E. E., Tomek, K. J., & Whitehead, T. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109105 · Cited by 95
Zhu, M., Mori, M., Hwa, T., & Dai, X. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0543-1 · Cited by 85
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Chen, D. J., & Arkin, A. P. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.52 · Cited by 88
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Cataudella, I., Trusina, A., Sneppen, K., Gerdes, K., & Mitarai, N. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks297 · Cited by 87
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Okano, H., Hermsen, R., Kochanowski, K., & Hwa, T. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0610-7 · Cited by 85
Metadata
- Authors
- Klumpp, S., Zhang, Z., & Hwa, T.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Klumpp, Stefan
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2009-12
- Volume
- 139
- Issue
- 7
- Pages
- 1366-1375
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC2818994
- OpenAlex
- W2115606218
- Cited by
- 703
- References
- 77
- Citations by year
- 2026: 47 · 2025: 41 · 2024: 37 · 2023: 40 · 2022: 54 · 2021: 36 · 2020: 44 · 2019: 48 · 2018: 53 · 2017: 36 · 2016: 40 · 2015: 53 · 2014: 47 · 2013: 51 · 2012: 34
- FWCI
- 10.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM077298, R01-GM77298)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- bacterial growth rate, constitutive expression, synthetic gene circuits, ribosome abundance, feedback control, antibiotic resistance, antibiotic tolerance, persistence
- MeSH terms
- Escherichia coli, Homeostasis, Gene Expression Regulation, Bacterial, Feedback, Physiological, Regulatory Elements, Transcriptional, Gene Regulatory Networks
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