The cyanobacterial circadian clock follows midday in vivo and in vitro
- University of Chicago, United States
- Cited 10
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Abstract
Circadian rhythms are biological oscillations that schedule daily changes in physiology. Outside the laboratory, circadian clocks do not generally free-run, but are driven by daily cues whose timing varies with the seasons. The principles that determine how circadian clocks align to these external cycles are not well understood. Here we report experimental platforms for driving the cyanobacterial circadian clock both in vivo and in vitro. We find that the phase of the circadian rhythm follows a simple scaling law in light-dark cycles, tracking midday across conditions with variable day length. The core biochemical oscillator comprised of the Kai proteins behaves similarly when driven by metabolic pulses in vitro, indicating that such dynamics are intrinsic to these proteins. We develop a general mathematical framework based on instantaneous transformation of the clock cycle by external cues, and it successfully predicts clock behavior under many cycling environments.
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Author details
Funding
Pew Charitable Trusts
- Michael J Rust
National Institute of General Medical Sciences (R01GM107369-01)
- Michael J Rust
National Science Foundation (PHY-1305542)
- Aaron R Dinner
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Naama Barkai, Weizmann Institute of Science, Israel
Publication history
- Received: November 22, 2016
- Accepted: July 6, 2017
- Accepted Manuscript published: July 7, 2017 (version 1)
- Version of Record published: September 19, 2017 (version 2)
Copyright
© 2017, Leypunskiy et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Computational and Systems Biology
- Microbiology and Infectious Disease
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- Cancer Biology
- Computational and Systems Biology
