title: Quantifying Stochastic Noise in Cultured Circadian Reporter Cells.
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.
authors.affiliation: Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado, United States of America.
authors.forename: Peter C
authors.initials: PC
authors.lastname: St John
authors.affiliation: Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America.
authors.forename: Francis J
authors.initials: FJ
authors.lastname: Doyle
mesh_terms: D000818:Animals; D002478:Cells, Cultured; D002940:Circadian Rhythm / Q000187:drug effects / Q000502:physiology*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D055785:Gene Knockdown Techniques; D051379:Mice; D008954:Models, Biological; D034622:RNA Interference / Q000502:physiology; D034741:RNA, Small Interfering / Q000494:pharmacology; D013269:Stochastic Processes
chemical_list: D034741:RNA, Small Interfering