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Combinatorial Gene Regulation Using Auto-Regulation

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  • Rutger Hermsen
  • Bas Ursem
  • Pieter Rein ten Wolde

Abstract

As many as 59% of the transcription factors in Escherichia coli regulate the transcription rate of their own genes. This suggests that auto-regulation has one or more important functions. Here, one possible function is studied. Often the transcription rate of an auto-regulator is also controlled by additional transcription factors. In these cases, the way the expression of the auto-regulator responds to changes in the concentrations of the “input” regulators (the response function) is obviously affected by the auto-regulation. We suggest that, conversely, auto-regulation may be used to optimize this response function. To test this hypothesis, we use an evolutionary algorithm and a chemical–physical model of transcription regulation to design model cis-regulatory constructs with predefined response functions. In these simulations, auto-regulation can evolve if this provides a functional benefit. When selecting for a series of elementary response functions—Boolean logic gates and linear responses—the cis-regulatory regions resulting from the simulations indeed often exploit auto-regulation. Surprisingly, the resulting constructs use auto-activation rather than auto-repression. Several design principles show up repeatedly in the simulation results. They demonstrate how auto-activation can be used to generate sharp, switch-like activation and repression circuits and how linearly decreasing response functions can be obtained. Auto-repression, on the other hand, resulted only when a high response speed or a suppression of intrinsic noise was also selected for. The results suggest that, while auto-repression may primarily be valuable to improve the dynamical properties of regulatory circuits, auto-activation is likely to evolve even when selection acts on the shape of response function only.Author Summary: Bacteria adjust which proteins they make, and how many copies of each kind, depending on their environment. The production rate of each regulated protein is controlled by a special class of proteins called transcription factors. The rate at which a certain protein is produced usually depends on the cellular concentrations of a few such transcription factors. When circumstances change, the concentrations of these transcription factors alter too and consequently the production rates of all proteins regulated by them are adjusted. Interestingly, many transcription factors also regulate their own synthesis rate. This suggests that this self-regulation must have one or more important functions. In this article we study one possible function. In order for cells to function properly each protein concentration has to respond in a particular way to changes in transcription factor concentrations. We have studied how bacteria can optimize and fine-tune these responses. To this end, we formulated a physical model of the regulation by transcription factors and performed computer simulations. These simulations show that self-regulation—and in particular self-activation—is often a useful tool to achieve the prescribed response. Therefore we conclude that natural selection on the regulation of protein levels could naturally lead to self-regulation.

Suggested Citation

  • Rutger Hermsen & Bas Ursem & Pieter Rein ten Wolde, 2010. "Combinatorial Gene Regulation Using Auto-Regulation," PLOS Computational Biology, Public Library of Science, vol. 6(6), pages 1-13, June.
  • Handle: RePEc:plo:pcbi00:1000813
    DOI: 10.1371/journal.pcbi.1000813
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    References listed on IDEAS

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    1. Matthew Freeman, 2000. "Feedback control of intercellular signalling in development," Nature, Nature, vol. 408(6810), pages 313-319, November.
    2. Mark Ptashne & Alexander Gann, 1997. "Transcriptional activation by recruitment," Nature, Nature, vol. 386(6625), pages 569-577, April.
    3. Attila Becskei & Luis Serrano, 2000. "Engineering stability in gene networks by autoregulation," Nature, Nature, vol. 405(6786), pages 590-593, June.
    4. Rutger Hermsen & Sander Tans & Pieter Rein ten Wolde, 2006. "Transcriptional Regulation by Competing Transcription Factor Modules," PLOS Computational Biology, Public Library of Science, vol. 2(12), pages 1-9, December.
    5. Michael B. Elowitz & Stanislas Leibler, 2000. "A synthetic oscillatory network of transcriptional regulators," Nature, Nature, vol. 403(6767), pages 335-338, January.
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    Cited by:

    1. Chen Jia & Ramon Grima, 2024. "Holimap: an accurate and efficient method for solving stochastic gene network dynamics," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Rutger Hermsen & David W Erickson & Terence Hwa, 2011. "Speed, Sensitivity, and Bistability in Auto-activating Signaling Circuits," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-9, November.

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