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PreprintCited by 11Gene Regulatory Network Analysisstochastic dynamics and bifurcationStochastic processes and statistical mechanics

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Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

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Zhang, Zhanhao, Zabaikina, Iryna, Nieto, César, Vahdat, Zahra, Bokes, Pavol, Singh, Abhyudai·2024·PLoS Computational Biology
DOI 10.1101/2024.06.28.601263PMC PMC12151482

Preprint — published as 10.1371/journal.pcbi.1013014 · Paper page

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Abstract

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

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Metadata

Authors
Zhang, Z., Zabaikina, I., Nieto, C., Vahdat, Z., Bokes, P., & Singh, A.
Affiliations
  • University of Delaware (US)
  • Comenius University Bratislava (SK)
  • Baylor College of Medicine (US)
Corresponding author
Singh, Abhyudai
ORCID
Venue
PLoS Computational Biology
Publisher
openRxiv
Published
2024-06-29
Volume
21
Issue
6
Pages
e1013014-e1013014
ISSN
1553-734X (ISSN-L)
DOI
10.1101/2024.06.28.601263
PMC
PMC12151482
OpenAlex
W4411171556
Cited by
11
References
1
Citations by year
2026: 5 · 2025: 4 · 2024: 2
FWCI
2.67
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Agentúra na Podporu Výskumu a Vývoja (APVV-18?, APVV-23-0039, APVV-18-0308)
  • Vedecká Grantová Agentúra MŠVVaŠ SR a SAV (1/0755/22)
  • National Institutes of Health
  • National Institute of General Medical Sciences (R35GM148351)
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
stochastic gene expression, single-cell gene expression, positive feedback, protein dilution, cell cycle, cell proliferation

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