The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Encoding and Decoding Cellular Information through Signaling Dynamics
Open accessAbstract
A growing number of studies are revealing that cells can send and receive information by controlling the temporal behavior (dynamics) of their signaling molecules. In this review, we discuss what is known about the dynamics of various signaling networks and their role in controlling cellular responses. We identify general principles that are emerging in the field, focusing specifically on how the identity and quantity of a stimulus is encoded in temporal patterns, how signaling dynamics influence cellular outcomes and how specific dynamical patterns are both shaped and interpreted by the structure of molecular networks. We conclude by discussing potential functional roles for transmitting cellular information through signaling dynamics and possible applications for the treatment of disease.
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Cites
Network motifs: theory and experimental approaches
Alon, U. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2102 · Cited by 3,279
Oscillations in NF-κB Signaling Control the Dynamics of Gene Expression
Nelson, D. E., Ihekwaba, A. E. C., Elliott, M., Johnson, J. R., Gibney, C. A., Foreman, B. E., Nelson, G., See, V., Horton, C. A., Spiller, D. G., et al. (2004) · Science · doi:10.1126/science.1099962 · Cited by 1,186
ATM- and cell cycle-dependent regulation of ATR in response to DNA double-strand breaks
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Small-molecule MDM2 antagonists reveal aberrant p53 signaling in cancer: implications for therapy
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Pulsatile Stimulation Determines Timing and Specificity of NF-κB-Dependent Transcription
Ashall, L., Horton, C. A., Nelson, D. E., Paszek, P., Harper, C. V., Sillitoe, K., Ryan, S., Spiller, D. G., Unitt, J. F., Broomhead, D. S., et al. (2009) · Science · doi:10.1126/science.1164860 · Cited by 543
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Cited by
Deep learning for cellular image analysis
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225
Inferring biological networks by sparse identification of nonlinear dynamics
Mangan, N. M., Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016) · IEEE Transactions on Molecular, Biological and Multi-Scale Communications · doi:10.1109/tmbmc.2016.2633265 · Cited by 436
Cell-cell communication: New insights and clinical implications
Su, J., Song, Y., Zhu, Z., Huang, X., Fan, J., Qiao, J., & Mao, F. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01888-z · Cited by 433
Barr, A. R., Cooper, S., Heldt, F. S., Butera, F., Stoy, H., Mansfeld, J., Novák, B., & Bakal, C. (2017) · Nature Communications · doi:10.1038/ncomms14728 · Cited by 403
Principles of Ubiquitin-Dependent Signaling
Oh, E., Akopian, D., & Rape, M. (2018) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100617-062802 · Cited by 339
Metadata
- Authors
- Purvis, J. E., & Lahav, G.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 152
- Issue
- 5
- Pages
- 945-956
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W1992182416
- Cited by
- 798
- References
- 7
- FWCI
- 25.42
- Type
- journal article
- Language
- en
- Open access
- bronze
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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).
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