Inference and uncertainty quantification of stochastic gene expression via synthetic models

Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accu...

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Bibliographic Details
Published in:Journal of the Royal Society interface 2022-07, Vol.19 (192), p.20220153
Main Authors: Öcal, Kaan, Gutmann, Michael U, Sanguinetti, Guido, Grima, Ramon
Format: Article
Language:English
Subjects:
Algorithms
Gene Expression
Life Sciences–Mathematics interface
Models, Biological
Stochastic Processes
Uncertainty
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Summary:Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a , to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.
ISSN:1742-5662
1742-5689
1742-5662
DOI:10.1098/rsif.2022.0153