The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.
A Stochastic Single-Molecule Event Triggers Phenotype Switching of a Bacterial Cell
Open accessAbstract
By monitoring fluorescently labeled lactose permease with single-molecule sensitivity, we investigated the molecular mechanism of how an Escherichia coli cell with the lac operon switches from one phenotype to another. At intermediate inducer concentrations, a population of genetically identical cells exhibits two phenotypes: induced cells with highly fluorescent membranes and uninduced cells with a small number of membrane-bound permeases. We found that this basal-level expression results from partial dissociation of the tetrameric lactose repressor from one of its operators on looped DNA. In contrast, infrequent events of complete dissociation of the repressor from DNA result in large bursts of permease expression that trigger induction of the lac operon. Hence, a stochastic single-molecule event determines a cell's phenotype.
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
Genetic regulatory mechanisms in the synthesis of proteins.
Jacob, F., & Monod, J. (1961) · Journal of Molecular Biology · doi:10.1016/s0022-2836(61)80072-7 · Cited by 5,300
Stochasticity in gene expression: from theories to phenotypes
Kærn, M., Elston, T. C., Blake, W. J., & Collins, J. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1615 · Cited by 2,318
Arkin, A. P., Ross, J., & McAdams, H. H. (1998) · Genetics · doi:10.1093/genetics/149.4.1633 · Cited by 1,492
Stochastic protein expression in individual cells at the single molecule level
Cai, L., Friedman, N., & Xie, X. S. (2006) · Nature · doi:10.1038/nature04599 · Cited by 1,145
Self-citationGene Regulation at the Single-Cell Level
Rosenfeld, N., Young, J. W., Alon, U., Swain, P. S., & Elowitz, M. B. (2005) · Science · doi:10.1126/science.1106914 · Cited by 1,104
Probing Transcription Factor Dynamics at the Single-Molecule Level in a Living Cell
Elf, J., Li, G., & Xie, X. (2007) · Science · doi:10.1126/science.1141967 · Cited by 1,063
Self-citationMultistability in the lactose utilization network of Escherichia coli
Ozbudak, E. M., Thattai, M., Lim, H. N., Shraiman, B. I., & van Oudenaarden, A. (2004) · Nature · doi:10.1038/nature02298 · Cited by 1,030
Probing Gene Expression in Live Cells, One Protein Molecule at a Time
Yu, J., Xiao, J., Ren, X., Lao, K., & Xie, X. S. (2006) · Science · doi:10.1126/science.1119623 · Cited by 918
Self-citationENZYME 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
Optimality and evolutionary tuning of the expression level of a protein
Dekel, E., & Alon, U. (2005) · Nature · doi:10.1038/nature03842 · Cited by 766
Flagged first, then by citations
1 of the 28 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 401–438 of 438
Páldi, A. (2018) · Life Sciences, Information Sciences · doi:10.1002/9781119452713.ch4
Most Probable Evolution Trajectories in a Genetic Regulatory System Excited by Stable Lévy NoisePreprint
Cheng, X., Wang, H., Wang, X., Duan, J., & Xiao-fan, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.09913
Cantú-González, J. R., Díaz-Hernández, O., Ramírez-Álvarez, E., Enriquez-Flores, C. I., Rosas, A., & Santos, G. J. E. (2019) · Advances in Computational Mathematics · doi:10.1007/s10444-018-09661-x
Natural reward as the fundamental macroevolutionary forcePreprint
Gilbert, O. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.09567
Zusammenspiel von Sensoren und Transportern unter Nährstofflimitation
Jung, K. (2019) · BIOspektrum · doi:10.1007/s12268-019-1084-0
Reconstructing an epigenetic landscape using a genetic pulling approach
Assaf, M., Be’er, S., & Roberts, E. (2021) · Physical review. E · doi:10.1103/physreve.103.062404
Choudhary, K., & Narang, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.09.940452
Faculty Opinions recommendation of Random sequences rapidly evolve into de novo promoters.
Mazel, D. (2018) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.733066720.793545543
Hsieh, C.-C., Li, C., & Shu, C. (2022) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2022.104330
MILTON, J. G., & Ohira, T. (2014) · Springer eBooks · doi:10.1007/978-1-4614-9096-8_12
Gerasimenko, J. V., & Lewis, S. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735949562.793564206
Cohen, G. N. (2014) · Springer eBooks · doi:10.1007/978-94-017-8908-0_16
Cohen, G. N. (2016) · Springer eBooks · doi:10.1007/978-94-017-7579-3_16
Cohen, G. N. (2010) · Springer eBooks · doi:10.1007/978-90-481-9437-7_16
(2008) · Nature Chemical Biology · doi:10.1038/nchembio1208-727
Persisters of Escherichia coli
Nicolau, S. E. (2021) · doi:10.17760/d20423509
Prions and prion-like phenomena in epigenetic inheritance
Silar, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91909-8.00013-x
Exact Computation of Growth-Rate Variance in Randomly Fluctuating Environment
Unterberger, J. M. (2022) · Journal of Statistical Physics · doi:10.1007/s10955-022-03002-5 · Cited by 1
Optimal Cellular Phenotypic Adaptation in Fluctuating EnvironmentsPreprint
George, J. T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.17.524479
Ge, H. (2023) · Scientia Sinica Mathematica · doi:10.1360/ssm-2023-0038
Regulatory Role of GgaR (YegW) for Glycogen Accumulation in Escherichia coli K-12
Saito, S., Kobayashi, I., Hoshina, M., Uenaka, E., Sakurai, A., Imamura, S., & Shimada, T. (2024) · Microorganisms · doi:10.3390/microorganisms12010115
A dual fluorescence protein expression system detects cell cycle dependent protein noise
Murugesan, A., Al-Shagrawi, R. A., Ramesh, T., & Kandhavelu, M. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.130262
Song, Q., Mi, L., Zhang, M., & Guo, Y. (2024) · Fluctuation and Noise Letters · doi:10.1142/s021947752550018x
Bulgheresi, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-18789-6.00005-3
Barik, D., Bhattacharjya, P., & Das, S. (2025) · Physical review. E · doi:10.1103/physreve.111.054219
Particle-based simulation of non-elementary bimolecular kinetics
Kearney, T., & Flegg, M. B. (2025) · Mathematical Biosciences · doi:10.1016/j.mbs.2025.109582
Agsu, G. G., Cao, Y., Jain, D., Strawbridge, S. E., Daly, S., Zhang, Z., Wurmser, A., Bowers, K., de Almeida, I. O., Nikolényi, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.03.691924
Screening and improving light-up RNA aptamer properties using droplet-based microfluidics
Autour, A. (2018) · theses.fr (ABES) · doi:10.70675/575c94f4ze9f5z4420z8a10z5a3d243d0c1f
Studying the variability ofbacterial growth in microfluidicdroplets
Barizien, A. (2019) · theses.fr (ABES) · doi:10.70675/de7c7b62zbd65z493aza218zc92b17c5d8f1
Expression stochastique des gènes chez Bacillus subtilis
Deloupy, A. (2018) · theses.fr (ABES) · doi:10.70675/93a8dd64z144bz4d4bza6f9zb5b664984135
Tchouanti, J. (2021) · theses.fr (ABES) · doi:10.70675/d6e03482zc2b5z4b51z9d1bz196800932e43
A binomial approach to the stochastic kinetics of simple chemical reactions
Lente, G. (2026) · Journal of Mathematical Chemistry · doi:10.1007/s10910-026-01820-6
Azevedo, M. M., Arsh, A. M., Jagadeesan, R., Ravaria, P., & Ribeiro, A. S. (2026) · mSystems · doi:10.1128/msystems.00808-26
Metadata
- Authors
- Choi, P. J., Cai, L., Frieda, K., & Xie, X. S.
- Affiliations
- Harvard University (US)
- Corresponding author
- Choi, Paul J.
- Cai, Long
- Frieda, Kirsten
- Xie, X. Sunney
- Venue
- Science
- Published
- 2008-10-17
- Volume
- 322
- Issue
- 5900
- Pages
- 442-446
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC2819113
- OpenAlex
- W1978182054
- Cited by
- 438
- References
- 28
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 13 · 2023: 11 · 2022: 11 · 2021: 14 · 2020: 21 · 2019: 33 · 2018: 28 · 2017: 26 · 2016: 30 · 2015: 27 · 2014: 35 · 2013: 34 · 2012: 37
- FWCI
- 19.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (DP1OD000277)
- National Institute of Biomedical Imaging and Bioengineering (R01EB010244)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Diffusion and Search Dynamics
- Keywords
- lac operon, lactose permease, lactose repressor, phenotypic switching, stochastic gene expression, single-molecule fluorescence, Escherichia coli, basal expression, gene expression regulation
- MeSH terms
- Bacterial Proteins, Escherichia coli, Lac Operon, Lactose, Microscopy, Fluorescence, Models, Genetic, Monosaccharide Transport Proteins, Operator Regions, Genetic, Phenotype, Recombinant Fusion Proteins, Repressor Proteins, RNA, Messenger, Stochastic Processes, Gene Expression Regulation, Bacterial, Feedback, Physiological, Symporters, Escherichia coli Proteins, Lac Repressors
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