The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Models of stochastic gene expression
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Citations
Cites
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
Intrinsic and extrinsic contributions to stochasticity in gene expression
Swain, P. S., Elowitz, M. B., & Siggia, E. D. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.162041399 · Cited by 1,661
Intrinsic noise in gene regulatory networks
Thattai, M., & van Oudenaarden, A. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.151588598 · Cited by 1,428 · cited 3×
“At the same time, the same model was independently analyzed by Thattai and van Oudenaarden [19] who calculated time-dependent moments and emphasized translation bursts, but also treated auto systems as well as noise propagation in longer expression cascades [34].”
BackgroundResultControl of Stochasticity in Eukaryotic Gene Expression
Raser, J. M., & O'Shea, E. K. (2004) · Science · doi:10.1126/science.1098641 · Cited by 1,425
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Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Mammalian genes are transcribed with widely different bursting kinetics
Suter, D. M., Molina, N., Gatfield, D., Schneider, K., Schibler, U., & Naef, F. (2011) · Science · doi:10.1126/science.1198817 · Cited by 927
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
Noise in protein expression scales with natural protein abundance
Bar-Even, A., Paulsson, J., Maheshri, N., Carmi, M., O'Shea, E., Pilpel, Y., & Barkai, N. (2006) · Nature Genetics · doi:10.1038/ng1807 · Cited by 807
Self-citationEffects of Molecular Memory and Bursting on Fluctuations in Gene Expression
Pedraza, J. M., & Paulsson, J. (2008) · Science · doi:10.1126/science.1144331 · Cited by 404
Self-citationEukaryotic transcriptional dynamics: from single molecules to cell populations
Coulon, A., Chow, C. C., Singer, R. H., & Larson, D. R. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3484 · Cited by 309
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
Gorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186
Digital Quantification of Proteins and mRNA in Single Mammalian Cells
Albayrak, C., Jordi, C. A., Zechner, C., Lin, J., Bichsel, C. A., Khammash, M., & Tay, S. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.02.030 · Cited by 176
Bursts and pulses: Insights from single cell studies into transcriptional mechanisms
Chubb, J. R., & Liverpool, T. B. (2010) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2010.06.009 · Cited by 91
Gene silencing with CRISPRi in bacteria and optimization of dCas9 expression levels
Depardieu, F., & Bikard, D. (2020) · Methods · doi:10.1016/j.ymeth.2019.07.024 · Cited by 86
The correlation between cell and nucleus size is explained by an eukaryotic cell growth model
Wu, Y., Pegoraro, A. F., Weitz, D. A., Janmey, P., & Sun, S. X. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009400 · Cited by 72
Robust replication initiation from coupled homeostatic mechanisms
Berger, M., & Wolde, P. R. T. (2022) · Nature Communications · doi:10.1038/s41467-022-33886-6 · Cited by 41
Mechanisms of transcription control by distal enhancers from high-resolution single-gene imaging
Cheng, L., De, C., Li, J., & Pertsinidis, A. (2023) · doi:10.1101/2023.03.19.533190 · Cited by 32
Effects of cell-cycle-dependent expression on random fluctuations in protein levels
Soltani, M., & Singh, A. (2016) · Royal Society Open Science · doi:10.1098/rsos.160578 · Cited by 32
What can we learn when fitting a simple telegraph model to a complex gene expression model?
Jiao, F., Li, J., Liu, T., Zhu, Y., Che, W., Bleris, L., & Jia, C. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012118 · Cited by 26
Zhang, Z., Zabaikina, I., Nieto, C., Vahdat, Z., Bokes, P., & Singh, A. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013014 · Cited by 11
Liu, S., Wu, B., & Jia, C. (2025) · SIAM Journal on Applied Mathematics · doi:10.1137/24m1671566 · 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
- Paulsson, J.
- Venue
- Physics of Life Reviews
- Publisher
- Elsevier BV
- Published
- 2005-06
- Volume
- 2
- Issue
- 2
- Pages
- 157-175
- ISSN
- 1571-0645 (ISSN-L)
- OpenAlex
- W2122121413
- Cited by
- 787
- References
- 4
- FWCI
- 6.45
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
