Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Gene network shaping of inherent noise spectra

Abstract

Recent work demonstrates that stochastic fluctuations in molecular populations have consequences for gene regulation1,2,3,4,5,6,7,8,9,10. Previous experiments focused on noise sources or noise propagation through gene networks by measuring noise magnitudes. However, in theoretical analysis, we showed that noise frequency content is determined by the underlying gene circuits, leading to a mapping between gene circuit structure and the noise frequency range11,12. An intriguing prediction from our previous studies was that negative autoregulation shifts noise to higher frequencies where it is more easily filtered out by gene networks11—a property that may contribute to the prevalence of autoregulation motifs (for example, found in the regulation of 40% of Escherichia coli genes). Here we measure noise frequency content in growing cultures of E. coli, and verify the link between gene circuit structure and noise spectra by demonstrating the negative autoregulation-mediated spectral shift. We further demonstrate that noise spectral measurements provide mechanistic insights into gene regulation, as perturbations of gene circuit parameters are discernible in the measured noise frequency ranges. These results suggest that noise spectral measurements could facilitate the discovery of novel regulatory relationships.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Measurement of noise frequency ranges using fluorescence time-lapse microscopy.
Figure 2: Effects of cell doubling time and protein half-life on noise frequency range.
Figure 3: Effect of negative autoregulation on noise frequency range.
Figure 4: Regulation strength modulation of noise frequency range.

Similar content being viewed by others

References

  1. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002)

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Rosenfeld, N., Young, J. W., Alon, U., Swain, P. S. & Elowitz, M. B. Gene regulation at the single-cell level. Science 307, 1962–1965 (2005)

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl Acad. Sci. USA 99, 12795–12800 (2002)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Blake, W. J., Kaern, M., Cantor, C. R. & Collins, J. J. Noise in eukaryotic gene expression. Nature 422, 633–637 (2003)

    Article  ADS  CAS  PubMed  Google Scholar 

  5. Raser, J. M. & O'Shea, E. K. Control of stochasticity in eukaryotic gene expression. Science 304, 1811–1814 (2004)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ozbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D. & van Oudenaarden, A. Regulation of noise in the expression of a single gene. Nature Genet. 31, 69–73 (2002)

    Article  CAS  PubMed  Google Scholar 

  7. Pedraza, J. M. & van Oudenaarden, A. Noise propagation in gene networks. Science 307, 1965–1969 (2005)

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Thattai, M. & van Oudenaarden, A. Intrinsic noise in gene regulatory networks. Proc. Natl Acad. Sci. USA 98, 8614–8619 (2001)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kaern, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: From theories to phenotypes. Nature Rev. Genet. 6, 451–464 (2005)

    Article  CAS  PubMed  Google Scholar 

  10. Rao, C. V., Wolf, D. M. & Arkin, A. P. Control, exploitation and tolerance of intracellular noise. Nature 420, 231–237 (2002)

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Simpson, M. L., Cox, C. D. & Sayler, G. S. Frequency domain analysis of noise in autoregulated gene circuits. Proc. Natl Acad. Sci. USA 100, 4551–4556 (2003)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Simpson, M. L., Cox, C. D. & Sayler, G. S. Frequency domain chemical Langevin analysis of stochasticity in gene transcriptional regulation. J. Theor. Biol. 229, 383–394 (2004)

    Article  CAS  PubMed  Google Scholar 

  13. Gillespie, D. T. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361 (1977)

    Article  CAS  Google Scholar 

  14. Gibson, M. A. & Bruck, J. Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem. A 104, 1876–1889 (2000)

    Article  CAS  Google Scholar 

  15. Andersen, J. B. et al. New unstable variants of green fluorescent protein for studies of transient gene expression in bacteria. Appl. Environ. Microbiol. 64, 2240–2246 (1998)

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Kepler, T. B. & Elston, T. C. Stochasticity in transcriptional regulation: Origins, consequences, and mathematical representations. Biophys. J. 81, 3116–3136 (2001)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cox, C. D. et al. Analysis of noise in quorum sensing. OMICS 7, 317–334 (2003)

    Article  CAS  PubMed  Google Scholar 

  18. Lutz, R. & Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Res. 25, 1203–1210 (1997)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Becskei, A. & Serrano, L. Engineering stability in gene networks by autoregulation. Nature 405, 590–593 (2000)

    Article  ADS  CAS  PubMed  Google Scholar 

  20. Arkin, A., Ross, J. & McAdams, H. H. Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 149, 1633–1648 (1998)

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Thattai, M. & van Oudenaarden, A. Stochastic gene expression in fluctuating environments. Genetics 167, 523–530 (2004)

    Article  PubMed  PubMed Central  Google Scholar 

  22. Becskei, A., Seraphin, B. & Serrano, L. Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO J. 20, 2528–2535 (2001)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Isaacs, F. J., Hasty, J., Cantor, C. R. & Collins, J. J. Prediction and measurement of an autoregulatory genetic module. Proc. Natl Acad. Sci. USA 100, 7714–7719 (2003)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  24. Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000)

    Article  ADS  CAS  PubMed  Google Scholar 

  25. Bendat, J. S. & Piersol, A. G. Data: Analysis and Measurement Procedures (Wiley, New York, 2000)

    MATH  Google Scholar 

Download references

Acknowledgements

We thank I. Golding and J. Dunlap for advice on sample preparation and imaging, and M. Elowitz, J. Collins and T. Gardner for the gift of plasmids. This work was supported by the National Academies Keck Futures Initiative, the DARPA Bio-Computation Program, the NSF, the DOE Office of Advanced Scientific Computing Research, and was a user project of the Oak Ridge National Laboratory (ORNL) Center for Nanophase Materials Sciences (CNMS). D.W.A. acknowledges support from an ORNL CNMS Research Scholar Fellowship. R.D.D. acknowledges support from the DOE Science Undergraduate Laboratory Internship program. Author Contributions D.W.A., M.S.A., J.M.M., C.D.C. and M.L.S. planned the experimental, analytical and computational work. D.W.A., R.D.D., and J.R.W. performed the time-lapse microscopy measurements. M.S.A., J.R.W. and G.S.S. were responsible for the synthetic biology. D.W.A., R.D.D., C.D.C., J.M.M. and M.L.S. analysed the experimental data. J.M.M., N.F.S. and C.D.C. were responsible for simulations. C.D.C. and M.L.S. developed the frequency domain analytical approach. M.L.S. was responsible for integration of experimental, analytical and computational components.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. L. Simpson.

Ethics declarations

Competing interests

Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.

Supplementary information

Supplementary Video

This movie shows fluorescence time-lapsed microscopy of one experiment using the pGFPasv gene circuit. The time lapse covers 8 hours with frames separated by 5 minutes. The yellow outline around one cell follows a single trajectory through six generations of cell division. The cell doubling time was approximately 90 minutes. (MOV 973 kb)

Supplementary Methods

Detailed methods are described here for the genetic constructs and cell strains; the extraction of noise frequency composition from fluorescence time-lapsed microscopy; and the analytical and computational modeling of the gene circuit noise properties. (DOC 2113 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Austin, D., Allen, M., McCollum, J. et al. Gene network shaping of inherent noise spectra. Nature 439, 608–611 (2006). https://doi.org/10.1038/nature04194

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature04194

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing