Estimating numbers of fluorescent molecules in single cells by analysing fluctuations in photobleaching

Bakker, Elco; Swain, Peter S.

doi:10.1101/272310

Cited by 2 publications

(2 citation statements)

References 63 publications

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“…Thus, we assume that absolute protein numbers are measured, which is in practice hampered by an unknown scaling factor between the observed fluorescence and the corresponding abundances, and by noise. While various suggestions for inferring that factor have been 7/9 made 5,26,27 , and while a linear scaling is customarily assumed 6,11 , an accurate experimental determination remains extremely challenging.…”

Section: Discussionmentioning

confidence: 99%

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

Veerman

Popović

Marr

2018

Preprint

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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 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 Bayesian 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. Our results indicate that the method showcased here may allow for successful parameter inference and model identification from longitudinal single cell data. 5/9

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Section: Discussionmentioning

confidence: 99%

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

Veerman

Popović

Marr

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…made 5,26,27 , and while a linear scaling is customarily assumed 6,11 , an accurate experimental determination remains extremely challenging.…”

Section: Author Contributions Statementmentioning

confidence: 99%

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

Veerman

Popović

Marr

2021

International Journal of Nonlinear Sciences and Numerical Simulation

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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.

show abstract

Estimating numbers of fluorescent molecules in single cells by analysing fluctuations in photobleaching

Cited by 2 publications

References 63 publications

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

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

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

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