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PreprintCited by 13Gene Regulatory Network AnalysisSingle-cell and spatial transcriptomicsGenomics and Chromatin Dynamics

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Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

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Nicoll, Andrew G., Szavits-Nossan, Juraj, Evans, Martin R., Grima, Ramon·2023·Nature Communications
DOI 10.1101/2023.12.30.573521PMC PMC11929856

Preprint — published as 10.1038/s41467-025-58127-4

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Abstract

What features of transcription can be learnt by fitting mathematical models of gene expression to mRNA count data? Given a suite of models, fitting to data selects an optimal one, thus identifying a probable transcriptional mechanism. Whilst attractive, the utility of this methodology remains unclear. Here, we sample steady-state, single-cell mRNA count distributions from parameters in the physiological range, and show they cannot be used to confidently estimate the number of inactive gene states, i.e. the number of rate-limiting steps in transcriptional initiation. Distributions from over 99% of the parameter space generated using models with 2, 3, or 4 inactive states can be well fit by one with a single inactive state. However, we show that for many minutes following induction, eukaryotic cells show an increase in the mean mRNA count that obeys a power law whose exponent equals the sum of the number of states visited from the initial inactive to the active state and the number of rate-limiting post-transcriptional processing steps. Our study shows that estimation of the exponent from eukaryotic data can be sufficient to determine a lower bound on the total number of regulatory steps in transcription initiation, splicing, and nuclear export.

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Metadata

Authors
Nicoll, A. G., Szavits-Nossan, J., Evans, M. R., & Grima, R.
Affiliations
University of Edinburgh (GB)
Corresponding author
Grima, Ramon
ORCID
Venue
Nature Communications
Publisher
openRxiv
Published
2023-12-30
Volume
16
Issue
1
Pages
2833-2833
ISSN
2041-1723 (ISSN-L)
DOI
10.1101/2023.12.30.573521
PMC
PMC11929856
OpenAlex
W4408726337
Cited by
13
References
1
Citations by year
2026: 6 · 2025: 6 · 2024: 1
FWCI
3.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (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)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Leverhulme Trust (RPG-2020-327)
  • Biotechnology and Biological Sciences Research Council
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
stochastic gene expression, transcription initiation, model selection, rate-determining step, splicing, nuclear export, eukaryotic transcription, post-transcriptional processing
MeSH terms
Gene Expression Regulation, Humans, Models, Genetic, RNA, Messenger, Stochastic Processes, Transcription, Genetic, Single-Cell Analysis

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