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PreprintCited by 26Gene Regulatory Network Analysisstochastic dynamics and bifurcationSingle-cell and spatial transcriptomics

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What can we learn when fitting a simple telegraph model to a complex gene expression model?

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Jiao, Feng, Li, Jing, Liu, Ting, Zhu, Yifeng, Che, Wenhao, Bleris, Leonidas, Jia, Chen·2023·PLoS Computational Biology
DOI 10.1101/2023.03.09.532005PMC PMC11125521

Preprint — published as 10.1371/journal.pcbi.1012118 · Paper page

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Abstract

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

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Metadata

Authors
Jiao, F., Li, J., Liu, T., Zhu, Y., Che, W., Bleris, L., & Jia, C.
Affiliations
  • Guangzhou University (CN)
  • The University of Texas at Dallas (US)
  • Beijing Computational Science Research Center (CN)
Corresponding author
Jia, Chen
ORCID
Venue
PLoS Computational Biology
Publisher
openRxiv
Published
2023-03-12
Volume
20
Issue
5
Pages
e1012118-e1012118
ISSN
1553-734X (ISSN-L)
DOI
10.1101/2023.03.09.532005
PMC
PMC11125521
OpenAlex
W4396886318
Cited by
26
References
1
Citations by year
2026: 10 · 2025: 15 · 2024: 1
FWCI
3.53
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Science Foundation (2029121)
  • National Natural Science Foundation of China (U2230402, No. U2230402, 12271020, 12271118, 12131005)
  • University of Texas at Dallas
  • National Safety Academic Fund (U2230402)
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
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
stochastic gene expression, genetic control, feedback regulation, multimodality, conditional distributions, extrinsic noise, parameter identifiability, Escherichia coli, mammalian cells
MeSH terms
Single-Cell Gene Expression Analysis, Animals, Escherichia coli, Models, Genetic, Proteins, RNA, Messenger, Gene Expression, Mice, Gene Regulatory Networks

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