Literature DB >> 34774502

Diverse role of decoys on emergence and precision of oscillations in a biomolecular clock.

Supravat Dey1, Abhyudai Singh2.   

Abstract

Biomolecular clocks are key drivers of oscillatory dynamics in diverse biological processes including cell-cycle regulation, circadian rhythms, and pattern formation during development. A minimal clock implementation is based on the classical Goodwin oscillator, in which a repressor protein inhibits its own synthesis via time-delayed negative feedback. Clock motifs, however, do not exist in isolation; its components are open to interacting with the complex environment inside cells. For example, there are ubiquitous high-affinity binding sites along the genome, known as decoys, where transcription factors such as repressor proteins can potentially interact. This binding affects the availability of transcription factors and has often been ignored in theoretical studies. How does such genomic decoy binding impact the clock's robustness and precision? To address this question, we systematically analyze deterministic and stochastic models of the Goodwin oscillator in the presence of reversible binding of the repressor to a finite number of decoy sites. Our analysis reveals that the relative stability of decoy-bound repressors compared to the free repressor plays distinct roles on the emergence and precision of oscillations. Interestingly, active degradation of the bound repressor can induce sustained oscillations that are otherwise absent without decoys. In contrast, decoy abundances can kill oscillation dynamics if the bound repressor is protected from degradation. Taking into account low copy-number fluctuations in clock components, we show that the degradation of the bound repressors enhances precision by attenuating noise in both the amplitude and period of oscillations. Overall, these results highlight the versatile role of otherwise hidden decoys in shaping the stochastic dynamics of biological clocks and emphasize the importance of synthetic decoys in designing robust clocks.
Copyright © 2021 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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Year:  2021        PMID: 34774502      PMCID: PMC8715246          DOI: 10.1016/j.bpj.2021.11.013

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  75 in total

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Authors:  Dmitri Bratsun; Dmitri Volfson; Lev S Tsimring; Jeff Hasty
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

2.  Precision of genetic oscillators and clocks.

Authors:  Luis G Morelli; Frank Jülicher
Journal:  Phys Rev Lett       Date:  2007-05-31       Impact factor: 9.161

3.  Signatures of nonlinearity in single cell noise-induced oscillations.

Authors:  Philipp Thomas; Arthur V Straube; Jens Timmer; Christian Fleck; Ramon Grima
Journal:  J Theor Biol       Date:  2013-07-02       Impact factor: 2.691

4.  Timing molecular motion and production with a synthetic transcriptional clock.

Authors:  Elisa Franco; Eike Friedrichs; Jongmin Kim; Ralf Jungmann; Richard Murray; Erik Winfree; Friedrich C Simmel
Journal:  Proc Natl Acad Sci U S A       Date:  2011-09-15       Impact factor: 11.205

5.  Avian hairy gene expression identifies a molecular clock linked to vertebrate segmentation and somitogenesis.

Authors:  I Palmeirim; D Henrique; D Ish-Horowicz; O Pourquié
Journal:  Cell       Date:  1997-11-28       Impact factor: 41.582

6.  Mathematics of cellular control processes. I. Negative feedback to one gene.

Authors:  J S Griffith
Journal:  J Theor Biol       Date:  1968-08       Impact factor: 2.691

7.  Pairing of segmentation clock genes drives robust pattern formation.

Authors:  Oriana Q H Zinani; Kemal Keseroğlu; Ahmet Ay; Ertuğrul M Özbudak
Journal:  Nature       Date:  2020-12-23       Impact factor: 49.962

8.  Persistence, period and precision of autonomous cellular oscillators from the zebrafish segmentation clock.

Authors:  Alexis B Webb; Iván M Lengyel; David J Jörg; Guillaume Valentin; Frank Jülicher; Luis G Morelli; Andrew C Oates
Journal:  Elife       Date:  2016-02-13       Impact factor: 8.140

9.  The Goodwin model: behind the Hill function.

Authors:  Didier Gonze; Wassim Abou-Jaoudé
Journal:  PLoS One       Date:  2013-08-01       Impact factor: 3.240

10.  Mitotic trigger waves and the spatial coordination of the Xenopus cell cycle.

Authors:  Jeremy B Chang; James E Ferrell
Journal:  Nature       Date:  2013-07-17       Impact factor: 49.962

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