Letter

Nature 454, 1119-1122 (28 August 2008) | doi:10.1038/nature07211; Received 14 April 2008; Accepted 25 June 2008; Published online 30 July 2008

Metabolic gene regulation in a dynamically changing environment

Matthew R. Bennett1,2,3, Wyming Lee Pang1,3,4, Natalie A. Ostroff1, Bridget L. Baumgartner1, Sujata Nayak1, Lev S. Tsimring2 & Jeff Hasty1,2

  1. Department of Bioengineering, and,
  2. Institute for Nonlinear Science, University of California, San Diego, La Jolla, California 92093, USA
  3. These authors contributed equally to this work.
  4. Present address: Institute for Systems Biology, Seattle, Washington 98103, USA.

Correspondence to: Jeff Hasty1,2 Correspondence and requests for materials should be addressed to J.H. (Email: hasty@ucsd.edu).

Natural selection dictates that cells constantly adapt to dynamically changing environments in a context-dependent manner. Gene-regulatory networks often mediate the cellular response to perturbation1, 2, 3, and an understanding of cellular adaptation will require experimental approaches aimed at subjecting cells to a dynamic environment that mimics their natural habitat4, 5, 6, 7, 8, 9. Here we monitor the response of Saccharomyces cerevisiae metabolic gene regulation to periodic changes in the external carbon source by using a microfluidic platform that allows precise, dynamic control over environmental conditions. We show that the metabolic system acts as a low-pass filter that reliably responds to a slowly changing environment, while effectively ignoring fast fluctuations. The sensitive low-frequency response was significantly faster than in predictions arising from our computational modelling, and this discrepancy was resolved by the discovery that two key galactose transcripts possess half-lives that depend on the carbon source. Finally, to explore how induction characteristics affect frequency response, we compare two S. cerevisiae strains and show that they have the same frequency response despite having markedly different induction properties. This suggests that although certain characteristics of the complex networks may differ when probed in a static environment, the system has been optimized for a robust response to a dynamically changing environment.

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