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Veerman, F.* ; Popović, N.* ; Marr, C.

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

Int. J. Nonlinear Sci. 23, 565–577 (2022)
Publ. Version/Full Text DOI
Open Access Gold (Paid Option)
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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Asymptotic Analysis ; Parameter Inference ; Propagator ; Stochastic Gene Expression
ISSN (print) / ISBN 1749-3889
e-ISSN 1749-3897
Journal International Journal of Nonlinear Science
Quellenangaben Volume: 23, Issue: 3-4, Pages: 565–577 Article Number: , Supplement: ,
Publisher World Academic Press
Publishing Place Genthiner Strasse 13, D-10785 Berlin, Germany
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants Leverhulme Trust