PreprintCited by 32Gene Regulatory Network Analysisstochastic dynamics and bifurcationStochastic processes and statistical mechanics

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Cell-cycle coupled expression minimizes random fluctuations in gene product levels

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Soltani, Mohammad, Singh, Abhyudai·2016·Royal Society Open Science

Preprint — published as 10.1098/rsos.160578 · Paper page

Abstract

Expression of many genes varies as a cell transitions through different cell-cycle stages. How coupling between stochastic expression and cell cycle impacts cell-to-cell variability (noise) in the level of protein is not well understood. We analyze a model, where a stable protein is synthesized in random bursts, and the frequency with which bursts occur varies within the cell cycle. Formulas quantifying the extent of fluctuations in the protein copy number are derived and decomposed into components arising from the cell cycle and stochastic processes. The latter stochastic component represents contributions from bursty expression and errors incurred during partitioning of molecules between daughter cells. These formulas reveal an interesting trade-off: cell-cycle dependencies that amplify the noise contribution from bursty expression also attenuate the contribution from partitioning errors. We investigate existence of optimum strategies for coupling expression to the cell cycle that minimize the stochastic component. Intriguingly, results show that a zero production rate throughout the cell cycle, with expression only occurring just before cell division minimizes noise from bursty expression for a fixed mean protein level. In contrast, the optimal strategy in the case of partitioning errors is to make the protein just after cell division. We provide examples of regulatory proteins that are expressed only towards the end of cell cycle, and argue that such strategies enhance robustness of cell-cycle decisions to the intrinsic stochasticity of gene expression.

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    “In contrast, CV2 is the “intrinsic noise” resulting from stochasticity in gene expression and partitioning processes, and is measured by subtracting the extrinsic noise from the total noise Having appropriately defined the noise level, we next compute it using moment equations.”
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Metadata

Authors
Soltani, M., & Singh, A.
Affiliations
University of Delaware (US)
Corresponding author
Singh, Abhyudai
Venue
Royal Society Open Science
Publisher
openRxiv
Published
2016-05-08
Volume
3
Issue
12
Pages
160578-160578
ISSN
2054-5703 (ISSN-L)
OpenAlex
W2560588933
Cited by
32
References
1
Citations by year
2026: 1 · 2025: 3 · 2024: 1 · 2023: 3 · 2022: 2 · 2021: 4 · 2020: 6 · 2019: 4 · 2018: 4 · 2017: 3 · 2016: 1
FWCI
2.08
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Royal Society Open Science (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
National Science Foundation (1312926)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
  • Physical Sciences › Mathematics › Mathematical Physics › Stochastic processes and statistical mechanics
Keywords
cell cycle, stochastic gene expression, cell-to-cell variability, cell division, protein synthesis

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