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A bottom-up approach to gene regulation

Nature volume 439, pages 856–860 (2006)Cite this article

Abstract

The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the ‘bottom up’. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation.

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Figure 1: Unregulated, repressor-only, activator-only and repressor–activator systems on high-copy plasmids.
Figure 2: Histograms of model data (blue lines) and experimental data (red lines) for the repressor–activator system on a low-copy plasmid.
Figure 3: Repressor–activator system with positive feedback.
Figure 4: CV versus arabinose levels for the repressor–activator system.

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Acknowledgements

This work was supported by the NIH, NSF and DARPA. Author Contributions T.C.E. and J.J.C. are co-senior authors.

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Author notes

  1. Nicholas J. Guido and Xiao Wang: *These authors contributed equally to this work

Authors and Affiliations

  1. Department of Biomedical Engineering, Bioinformatics Program, Center for BioDynamics and Center for Advanced Biotechnology, Boston University, 44 Cummington Street, Massachusetts, 02215, Boston, USA

    Nicholas J. Guido, Charles R. Cantor & J. J. Collins

  2. Department of Statistics and Operations Research,

    Xiao Wang

  3. Department of Mathematics,

    David Adalsteinsson

  4. Department of Pharmacology, University of North Carolina, North Carolina, 27599, Chapel Hill, USA

    Timothy C. Elston

  5. Institute for Optical Sciences and Department of Chemical and Physical Sciences, University of Toronto at Mississauga, 3359 Mississauga Rd, Ontario, L5L 1C6, Mississauga, Canada

    David McMillen

  6. Department of Bioengineering, University of California, 9500 Gillman Drive, California, 92093, San Diego, La Jolla, USA

    Jeff Hasty

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Correspondence to J. J. Collins.

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Supplementary Notes

This file contains text detailing modeling details and further experimental work omitted from the main text due to space restrictions. There are 13 Supplementary Figures and 3 Supplementary Tables. (PDF 811 kb)

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Guido, N., Wang, X., Adalsteinsson, D. et al. A bottom-up approach to gene regulation. Nature 439, 856–860 (2006). https://doi.org/10.1038/nature04473

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