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Inference and uncertainty quantification of stochastic gene expression via synthetic models
Authors:Kaan Ö  cal,Michael U. Gutmann,Guido Sanguinetti,Ramon Grima
Affiliation:1. School of Informatics, University of Edinburgh, Edinburgh EH9 3JH, UK ; 2. School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK ; 3. Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy
Abstract: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 synthetic model, 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.
Keywords:Bayesian inference   uncertainty quantification   chemical master equation   synthetic likelihoods   stochastic modelling
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