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Quantitative model for gene regulation by lambda phage repressor.
Open accessAbstract
A statistical thermodynamic model has been developed to account for the cooperative interactions of the bacteriophage lambda repressor with the lambda right operator. The model incorporates a general theory for quantitatively interpreting cooperative site-specific equilibrium binding data. Values for all interaction parameters of the model have been evaluated at 37 degrees C, 0.2 M KCl, from results of DNase protection experiments in vitro [A. D. Johnson, B. J. Meyer, & M. Ptashne, Proc. Natl. Acad. Sci. USA (1979) 76, 5061-5065]. With these values, the model predicts repression curves at the divergent promoters PR and PRM that control transcription of genes coding for the regulatory proteins cro and repressor, respectively. At physiological repressor concentrations, repression at PR is predicted to be nearly complete whereas PRM is predicted to remain highly active. The results demonstrate the importance of cooperative interactions between repressor dimers bound to the adjacent operator sites OR1 and OR2 in maintaining a stable lysogenic state and in allowing efficient switchover to the lytic state during induction.
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“BAF inhibition did not affect the binding of rTetR alone (Fig. S3K), suggesting that BAF is specifically recruited by VP4821”
From Single-molecule states link transcription factor binding to gene expression (2024) · cites this paper as [21]
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“Because VP48 is known to directly interact with chromatin-remodeling cofactors21–23”
From Single-molecule states link transcription factor binding to gene expression (2024) · cites this paper as [21]
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Citations
Cites
A new method for sequencing DNA.
Maxam, A. M., & Gilbert, W. (1977) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.74.2.560 · Cited by 7,929
McGhee, J. D., & von Hippel, P. H. (1974) · Journal of Molecular Biology · doi:10.1016/0022-2836(74)90031-x · Cited by 2,961
A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase
Sanger, F., & Coulson, A. R. (1975) · Journal of Molecular Biology · doi:10.1016/0022-2836(75)90213-2 · Cited by 2,709
DNAase footprinting a simple method for the detection of protein-DNA binding specificity
Galas, D. J., & Schmitz, A. (1978) · Nucleic Acids Research · doi:10.1093/nar/5.9.3157 · Cited by 2,109
Introduction to statistical thermodynamics
(1960) · Journal of the Franklin Institute · doi:10.1016/0016-0032(60)90133-2 · Cited by 1,157
How the λ repressor and cro work
Ptashne, M., Jeffrey, A., Johnson, A. D., Maurer, R., Meyer, B. J., Pabo, C. O., Roberts, T. M., & Sauer, R. T. (1980) · Cell · doi:10.1016/0092-8674(80)90383-9 · Cited by 452
Self-citationλ Repressor and cro—components of an efficient molecular switch
Johnson, A. D., Poteete, A. R., Lauer, G. D., Sauer, R. T., Ackers, G. K., & Ptashne, M. (1981) · Nature · doi:10.1038/294217a0 · Cited by 387
Self-citationInteractions between DNA-bound repressors govern regulation by the λ phage repressor
Johnson, A. D., Meyer, B. J., & Ptashne, M. (1979) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.76.10.5061 · Cited by 324
Self-citationRate-limiting steps in RNA chain initiation.
McClure, W. R. (1980) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.77.10.5634 · Cited by 308
Gene regulation at the right operator (OR) of bacteriophage λ
Maurer, R., Meyer, B. J., & Ptashne, M. (1980) · Journal of Molecular Biology · doi:10.1016/0022-2836(80)90302-2 · Cited by 241
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Items 401–500 of 590
Generalized Michaelis–Menten rate law with time-varying molecular concentrations
Lim, R., Martin, T. L. P., Chae, J., Kim, W. J., Ghim, C.-M., & Kim, P. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011711 · Cited by 6
Inferring Transcription Networks from Data
Floares, A. G., & Luludachi, I. (2013) · Springer eBooks · doi:10.1007/978-3-642-30574-0_20 · Cited by 6
Kinetic Model of the Lysogeny/Lysis Switch of Phage λ
Hui, D., & Luo, L. (2009) · Chinese Physics Letters · doi:10.1088/0256-307x/26/9/098701 · Cited by 6
Bobrowski, A., & Bogucki, R. (2008) · Studia Mathematica · doi:10.4064/sm189-3-6 · Cited by 6
EFFECT OF NON-CODING RNA ON BISTABILITY AND OSCILLATIONS IN THE mRNA-PROTEIN INTERPLAY
Zhdanov, V. P. (2010) · Biophysical Reviews and Letters · doi:10.1142/s1793048010001159 · Cited by 6
Morse, B. K., Michalczyk, R., & Kosturko, L. D. (1994) · Biochimie · doi:10.1016/0300-9084(94)90025-6 · Cited by 6
Anticipating response function in gene regulatory networks
Gautam, P., & Sinha, S. K. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0206 · Cited by 6
Non-linear Dynamics in Transcriptional Regulation: Biological Logic Gates
Frank, T. D., Cavadas, M. A. S., Nguyen, L. K., & Cheong, A. (2016) · SEMA SIMAI Springer series · doi:10.1007/978-3-319-33054-9_3 · Cited by 5
Forst, C. V. (2009) · Springer eBooks · doi:10.1007/978-1-4419-1327-2_6 · Cited by 8
Resolving the contributions of two cooperative mechanisms to the DNA Binding of AGT
Melikishvili, M., & Fried, M. G. (2015) · Biopolymers · doi:10.1002/bip.22684 · Cited by 6
Modeling of Genetic Regulatory Network in Stochastic π-Calculus
Maurin, M., Magnin, M., & Roux, O. H. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00727-9_27 · Cited by 6
A Stochastic Model of Gene Regulation Using the Chemical Master Equation
Booth, H. S., Burden, C. J., Hegland, M., & Santoso, L. M. (2007) · Birkhäuser Boston eBooks · doi:10.1007/978-0-8176-4558-8_7 · Cited by 6
λ -prophage induction modeled as a cooperative failure mode of lytic repression
Chia, N., Golding, I., & Goldenfeld, N. D. (2009) · Physical Review E · doi:10.1103/physreve.80.030901 · Cited by 6
Nakanishi, H., Pedersen, M., Alsing, A. K., & Sneppen, K. (2009) · Journal of Molecular Biology · doi:10.1016/j.jmb.2009.08.075 · Cited by 6
Bashkin, J., & Tullius, T. D. (1993) · Elsevier eBooks · doi:10.1016/b978-0-12-586500-5.50010-2 · Cited by 6
Hayat, S., Ostermann, K., Brusch, L., Pompe, W., & Rödel, G. (2006) · International Conference on Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS) · doi:10.1145/1315843.1315850 · Cited by 6
Zhang, Y., Palma, C. S. D., Chen, Z., Zarazúa‐Osorio, B., Fujita, M., & Igoshin, O. A. (2025) · mSystems · doi:10.1128/msystems.00072-25 · Cited by 5
Cutts, E., Egan, J. B., Dodd, I. B., & Shearwin, K. E. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa655 · Cited by 5
Linking molecular mechanisms to their evolutionary consequences: a primer
Grah, R., Guet, C. C., Tkačik, G., & Lagator, M. (2024) · Genetics · doi:10.1093/genetics/iyae191 · Cited by 5
SHARMA, R. R., & Cherayil, B. J. (2013) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2013/10/p10029 · Cited by 5
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Carletti, M., & Banerjee, M. (2019) · Mathematics · doi:10.3390/math7121204 · Cited by 5
The unreasonable effectiveness of equilibrium gene regulation through the cell cycle
Vilar, J. M. G., & Saiz, L. (2024) · Cell Systems · doi:10.1016/j.cels.2024.06.002 · Cited by 4
Transcriptional activation mechanisms of the PRM promoter of λ phage
Bakk, A. (2004) · Biophysical Chemistry · doi:10.1016/j.bpc.2004.12.008 · Cited by 5
Ruklisa, D., Brāzma, A., Čerāns, K., Schlitt, T., & Vīksna, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2909-z · Cited by 5
Keeping up with the ′omics: non-equilibrium models of gene regulation
Pincus, D. (2015) · BMC Biology · doi:10.1186/s12915-015-0117-5 · Cited by 5
Data‐based model and parameter evaluation in dynamic transcriptional regulatory networks
Cavelier, G., & Anastassiou, D. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20056 · Cited by 5
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Phillips, R. (2017) · The FASEB Journal · doi:10.1096/fj.201700594 · Cited by 5
The free-energy cost of interaction between DNA loops
Huang, L., Liu, Y., Yuan, Z., Zhou, T., & Yu, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-12765-x · Cited by 4
Li, X., Li, T., Li, C., & Li, T. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0070485 · Cited by 5
Mathematical aspects of the regulation of gene transcription by promoters
Zhdanov, V. P. (2016) · Mathematical Biosciences · doi:10.1016/j.mbs.2016.11.007 · Cited by 4
Milkevych, V., Karaman, E., Sahana, G., Janss, L., Cai, Z., & Lund, M. S. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab133 · Cited by 3
Bacteriophage Use in Molecular Biology and Biotechnology
Brown, N. M., & Cox, C. (2021) · Springer eBooks · doi:10.1007/978-3-319-41986-2_15 · Cited by 4
Models of Genetic Regulatory Networks
Schilstra, M. J., & Bolouri, H. (2004) · Natural computing series · doi:10.1007/978-3-662-06369-9_8 · Cited by 4
Distinguishing the rates of gene activation from phenotypic variations
Chen, Y., Lv, C., Li, F., & Li, T. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0172-0 · Cited by 4
de‐Leon, S. B. (2010) · Developmental Biology · doi:10.1016/j.ydbio.2010.06.020 · Cited by 6
A two-scale mathematical model for DNA transcription
Chiu, C., Fakhouri, W. D., Liu, N., Dayringer, E., Dresch, J. M., & Arnosti, D. N. (2012) · Mathematical Biosciences · doi:10.1016/j.mbs.2011.12.006 · Cited by 4
Mackey, M. C., Santillán, M., Tyran‐Kamińska, M., & Zeron, E. S. (2016) · Lecture notes on mathematical modelling in the life sciences · doi:10.1007/978-3-319-45318-7_6 · Cited by 4
Information propagation in a noisy gene cascade
Monteoliva, D., & Diambra, L. A. (2017) · Physical review. E · doi:10.1103/physreve.96.012403 · Cited by 4
Abstracted Stochastic Analysis of Type 1 Pili Expression in E.coli.
Kuwahara, H., Myers, C. J., & Samoilov, M. S. (2006) · Cited by 4
Bioprocess Kinetics and Modelling of Recombinant Fermentation
Ryu, D. D. Y., Kim, J., & Lee, S. B. (2001) · Biotechnology Set · doi:10.1002/9783527620999.ch15c · Cited by 4
Zhu, X. M., Yin, L., Hood, L., & Ao, P. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.q-bio/0403016 · Cited by 4
Footprinting Methods to Examine the Structure and Dynamics of Nucleic Acids
Takamoto, K., & Chance, M. R. (2006) · Wiley-VCH eBooks · doi:10.1002/3527600906.mcb.200300091 · Cited by 4
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Tkačik, G., & Bialek, W. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0712.4385 · Cited by 5
Individual‐site binding data and the energetics of protein–DNA interactions
Saroff, H. A. (1993) · Biopolymers · doi:10.1002/bip.360330903 · Cited by 4
Wang, C.-H., Mehta, P., & Bashor, C. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/469197 · Cited by 4
Biophysical models of cis-regulation as interpretable neural networksPreprint
Tareen, A., & Kinney, J. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.03560 · Cited by 4
Martinez-Corral, R., Park, M., Biette, K. M., Friedrich, D., Scholes, C., Khalil, A. S., Gunawardena, J., & DePace, A. H. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3985163 · Cited by 4
Connaghan, K. D., Yang, Q., Miura, M. T., Moody, A. D., & Bain, D. L. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24563 · Cited by 3
A Stochastic Model for Virus Growth in a Cell Population
Björnberg, J. E., Britton, T., Broman, E. I., & Natan, E. (2014) · Journal of Applied Probability · doi:10.1239/jap/1409932661 · Cited by 3
Xu, L., & Wang, J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817996-3.00002-5 · Cited by 3
Liu, Z., Gillis, T., Raman, S., & Cui, Q. (2023) · eLife · doi:10.7554/elife.92262 · Cited by 12
Escape from a metastable state in non-Markovian population dynamics
Vilk, O., & Assaf, M. (2024) · Physical review. E · doi:10.1103/physreve.110.044132 · Cited by 3
Dissecting Steroid Receptor Function by Analytical Ultracentrifugation
Bain, D. L., Angelis, R. W. D., Connaghan, K. D., Yang, Q., DeGala, G. D., & Lambert, J. R. (2015) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2015.04.005 · Cited by 2
Cloning the $\lambda$ Switch: Digital and Markov Representations
Ainuddin, U., Khurram, M., & Hasan, S. M. R. (2019) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2019.2908669 · Cited by 3
Noise-induced quasiperiod and period switching
Wu, Y., Jiao, Y., Zhao, Y., Jia, H., & Xu, L. (2022) · Physical review. E · doi:10.1103/physreve.105.014419 · Cited by 3
Kinetic proofreading of chromatin remodeling: from gene activation to gene repression and back
Singh, R. P., Brysbaert, G., Lensink, M. F., Cleri, F., & Blossey, R. (2015) · AIMS Biophysics · doi:10.3934/biophy.2015.4.398 · Cited by 3
Bacteriophage lambda repressor mediates the formation of a complex enhancer-like structure
Cui, L., Murchland, I., Dodd, I. B., & Shearwin, K. E. (2013) · Transcription · doi:10.4161/trns.26101 · Cited by 3
Pannala, V. R., Youseph, A. S. K., Bhartiya, S., & Venkatesh, K. V. (2011) · Systems and Synthetic Biology · doi:10.1007/s11693-011-9082-7 · Cited by 3
Dynamic model of gene regulation for the lac operon
Angelova, M. N., & Halim, A. B. (2011) · Journal of Physics Conference Series · doi:10.1088/1742-6596/286/1/012007 · Cited by 3
Self-organized Patterning by Diffusible Factors: Roles of a Community Effect
Batmanov, K., Kuttler, C., Lhoussaine, C., & Saka, Y. (2012) · Fundamenta Informaticae · doi:10.3233/fi-2012-723 · Cited by 4
Towards Synthetic Gene Circuits with Enhancers: Biology’s Multi-input Integrators
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Phenotype analysis using network motifs derived from changes in regulatory network dynamics
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Bakk, A., Metzler, R., & Sneppen, K. (2004) · Cited by 3
Hensley, P., & Smith, L. M. (1997) · Current Opinion in Biotechnology · doi:10.1016/s0958-1669(97)80150-4 · Cited by 3
Deciphering regulatory architectures from synthetic single-cell expression patternsPreprint
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Kozelka, J. (2010) · Analytical Biochemistry · doi:10.1016/j.ab.2010.09.023 · Cited by 1
Broccoli aptamer allows quantitative transcription regulation studies in vitro
van der Sijs, A., Visser, T., Moerman, P. G., Folkers, G. E., & Kegel, W. K. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304677 · Cited by 1
Single-cell technology for plant systems biology
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Dynamics of inducible genetic circuits
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Model of haplotype and phenotype in the evolution of a duplicated autoregulatory activator
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Metadata
- Authors
- Ackers, G. K., Johnson, A. D., & Shea, M. A.
- Affiliations
- Harvard University (US)
- ORCID
- Shea, M A
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 1982-02
- Volume
- 79
- Issue
- 4
- Pages
- 1129-1133
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC345914
- OpenAlex
- W2087234479
- Cited by
- 590
- References
- 25
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 17 · 2023: 9 · 2022: 14 · 2021: 19 · 2020: 22 · 2019: 22 · 2018: 22 · 2017: 15 · 2016: 26 · 2015: 17 · 2014: 22 · 2013: 32 · 2012: 26
- FWCI
- 2.05
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- 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 › Genomics and Chromatin Dynamics
- Keywords
- cooperative DNA binding, statistical thermodynamic model, site-specific DNA binding, divergent promoters, lysogeny, gene expression regulation
- MeSH terms
- DNA-Binding Proteins, Gene Expression Regulation, Genes, Viral, Lysogeny, Mathematics, Models, Genetic, Operon, Bacteriophage lambda, Repressor Proteins, Transcription Factors, Viral Proteins, Virus Activation, Viral Regulatory and Accessory Proteins
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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 ›
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