The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Designer gene networks: Towards fundamental cellular control
Open accessAbstract
The engineered control of cellular function through the design of synthetic genetic networks is becoming plausible. Here we show how a naturally occurring network can be used as a parts list for artificial network design, and how model formulation leads to computational and analytical approaches relevant to nonlinear dynamics and statistical physics. We first review the relevant work on synthetic gene networks, highlighting the important experimental findings with regard to genetic switches and oscillators. We then present the derivation of a deterministic model describing the temporal evolution of the concentration of protein in a single-gene network. Bistability in the steady-state protein concentration arises naturally as a consequence of autoregulatory feedback, and we focus on the hysteretic properties of the protein concentration as a function of the degradation rate. We then formulate the effect of an external noise source which interacts with the protein degradation rate. We demonstrate the utility of such a formulation by constructing a protein switch, whereby external noise pulses are used to switch the protein concentration between two values. Following the lead of earlier work, we show how the addition of a second network component can be used to construct a relaxation oscillator, whereby the system is driven around the hysteresis loop. We highlight the frequency dependence on the tunable parameter values, and discuss design plausibility. We emphasize how the model equations can be used to develop design criteria for robust oscillations, and illustrate this point with parameter plots illuminating the oscillatory regions for given parameter values. We then turn to the utilization of an intrinsic cellular process as a means of controlling the oscillations. We consider a network design which exhibits self-sustained oscillations, and discuss the driving of the oscillator in the context of synchronization. Then, as a second design, we consider a synthetic network with parameter values near, but outside, the oscillatory boundary. In this case, we show how resonance can lead to the induction of oscillations and amplification of a cellular signal. Finally, we construct a toggle switch from positive regulatory elements, and compare the switching properties for this network with those of a network constructed using negative regulation. Our results demonstrate the utility of model analysis in the construction of synthetic gene regulatory networks.
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Cites
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A synthetic oscillatory network of transcriptional regulators
Elowitz, M. B., & Leibler, S. (2000) · Nature · doi:10.1038/35002125 · Cited by 4,630
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 4,268
Self-citationFrom molecular to modular cell biology.
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Cited by
Stochasticity in Transcriptional Regulation: Origins, Consequences, and Mathematical Representations
Kepler, T. B., & Elston, T. C. (2001) · Biophysical Journal · doi:10.1016/s0006-3495(01)75949-8 · Cited by 907
Spatiotemporal control of gene expression with pulse-generating networks
Basu, S., Mehreja, R., Thiberge, S., Chen, M.-T., & Weiss, R. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307571101 · Cited by 458
Synthetic in vitro transcriptional oscillators
Kim, J., & Winfree, E. (2011) · Molecular Systems Biology · doi:10.1038/msb.2010.119 · Cited by 313
Metadata
- Authors
- Hasty, J., Isaacs, F., Dolnik, M., McMillen, D., & Collins, J. J.
- Venue
- Chaos: An Interdisciplinary Journal of Nonlinear Science
- Publisher
- AIP Publishing
- Published
- 2001-03-01
- Volume
- 11
- Issue
- 1
- Pages
- 207-220
- ISSN
- 1054-1500 (ISSN-L)
- 1089-7682
- OpenAlex
- W1990121351
- Cited by
- 268
- References
- 58
- FWCI
- 3.91
- Type
- journal article
- Language
- en
- Open access
- green
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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.