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Cited by 4Gene Regulatory Network Analysisstochastic dynamics and bifurcationStochastic processes and statistical mechanics

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Parameter inference with analytical propagators for stochastic models of autoregulated gene expression

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Veerman, Frits, Popović, Nikola, Marr, Carsten·2022·International Journal of Nonlinear Sciences and Numerical Simulation
DOI 10.1515/ijnsns-2019-0258

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Abstract

Stochastic gene expression in regulatory networks is conventionally modelled via the chemical master equation (CME). As explicit solutions to the CME, in the form of so-called propagators, are oftentimes not readily available, various approximations have been proposed. A recently developed analytical method is based on a separation of time scales that assumes significant differences in the lifetimes of mRNA and protein in the network, allowing for the efficient approximation of propagators from asymptotic expansions for the corresponding generating functions. Here, we showcase the applicability of that method to simulated data from a ‘telegraph’ model for gene expression that is extended with an autoregulatory mechanism. We demonstrate that the resulting approximate propagators can be applied successfully for parameter inference in the non-regulated model; moreover, we show that, in the extended autoregulated model, autoactivation or autorepression may be refuted under certain assumptions on the model parameters. These results indicate that our approach may allow for successful parameter inference and model identification from longitudinal single cell data.

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Metadata

Authors
Veerman, F., Popović, N., & Marr, C.
Affiliations
  • Heidelberg University (DE)
  • University of Edinburgh (GB)
  • Helmholtz Munich (DE)
Corresponding author
Popović, Nikola
ORCID
Venue
International Journal of Nonlinear Sciences and Numerical Simulation
Publisher
Walter de Gruyter GmbH
Published
2021-04-07
Volume
23
Issue
3-4
Pages
565-577
ISSN
  • 1565-1339 (ISSN-L)
  • 2191-0294
DOI
10.1515/ijnsns-2019-0258
OpenAlex
W3153152792
Cited by
4
References
3
Citations by year
2025: 1 · 2023: 1 · 2021: 1
FWCI
0.16
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Nonlinear Sciences and Numerical Simulation (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Site cant be reached (submittedVersion, other-oa)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders
Leverhulme Trust (RPG‐2015‐017, RPG-2015)
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, chemical master equation, time-scale separation, asymptotic expansion, generating functions, parameter inference, system identification, self-activation, autorepression, single-cell data, longitudinal data

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