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Quantifying Stochastic Noise in Cultured Circadian Reporter Cells

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  • Peter C St. John
  • Francis J Doyle III

Abstract

Stochastic noise at the cellular level has been shown to play a fundamental role in circadian oscillations, influencing how groups of cells entrain to external cues and likely serving as the mechanism by which cell-autonomous rhythms are generated. Despite this importance, few studies have investigated how clock perturbations affect stochastic noise—even as increasing numbers of high-throughput screens categorize how gene knockdowns or small molecules can change clock period and amplitude. This absence is likely due to the difficulty associated with measuring cell-autonomous stochastic noise directly, which currently requires the careful collection and processing of single-cell data. In this study, we show that the damping rate of population-level bioluminescence recordings can serve as an accurate measure of overall stochastic noise, and one that can be applied to future and existing high-throughput circadian screens. Using cell-autonomous fibroblast data, we first show directly that higher noise at the single-cell results in faster damping at the population level. Next, we show that the damping rate of cultured cells can be changed in a dose-dependent fashion by small molecule modulators, and confirm that such a change can be explained by single-cell noise using a mathematical model. We further demonstrate the insights that can be gained by applying our method to a genome-wide siRNA screen, revealing that stochastic noise is altered independently from period, amplitude, and phase. Finally, we hypothesize that the unperturbed clock is highly optimized for robust rhythms, as very few gene perturbations are capable of simultaneously increasing amplitude and lowering stochastic noise. Ultimately, this study demonstrates the importance of considering the effect of circadian perturbations on stochastic noise, particularly with regard to the development of small-molecule circadian therapeutics.Author Summary: As most organisms exist in an environment that changes predictably with a 24-hour period, highly optimized genetic circuits turn on and off the production of key regulatory proteins to anticipate the day/night cycle. In humans, the demands of a modern society have required that we deviate from this evolutionarily prescribed sleep and feeding schedule, resulting in increased long-term risks of metabolic disease. There is therefore a desire to find pharmacological treatments that would restore the normal functioning of our circadian clock despite irregular behavioral schedules. One aspect of these treatments that is often overlooked in searching for candidate drugs is how these treatments might affect the accuracy of the circadian timing system. Recording the time of each cell is possible but difficult; as a result single-cell approaches cannot be scaled up to high-throughput searches. In this paper, we show that it is possible to estimate how much the noise of a system has changed by looking only at the averaged protein production of an entire population of cells. Such an approach allows us to analyze prior data from high-throughput screens, and show that the natural clock has been highly optimized to be both accurate and high amplitude.

Suggested Citation

  • Peter C St. John & Francis J Doyle III, 2015. "Quantifying Stochastic Noise in Cultured Circadian Reporter Cells," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-17, November.
  • Handle: RePEc:plo:pcbi00:1004451
    DOI: 10.1371/journal.pcbi.1004451
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    1. Biliana Marcheva & Kathryn Moynihan Ramsey & Ethan D. Buhr & Yumiko Kobayashi & Hong Su & Caroline H. Ko & Ganka Ivanova & Chiaki Omura & Shelley Mo & Martha H. Vitaterna & James P. Lopez & Louis H. P, 2010. "Disruption of the clock components CLOCK and BMAL1 leads to hypoinsulinaemia and diabetes," Nature, Nature, vol. 466(7306), pages 627-631, July.
    2. Naama Barkai & Stanislas Leibler, 2000. "Circadian clocks limited by noise," Nature, Nature, vol. 403(6767), pages 267-268, January.
    3. Joseph Bass, 2012. "Circadian topology of metabolism," Nature, Nature, vol. 491(7424), pages 348-356, November.
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    1. Saverio Ranciati & Alberto Roverato & Alessandra Luati, 2021. "Fused graphical lasso for brain networks with symmetries," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1299-1322, November.

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