Back to Search
Start Over
Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models
- Source :
- Öcal, K, Gutmann, M U, Sanguinetti, G & Grima, R 2022, ' Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models ', Journal of the Royal Society. Interface, vol. 19, no. 192, 20220153 . https://doi.org/10.1098/rsif.2022.0153
- Publication Year :
- 2022
- Publisher :
- Cold Spring Harbor Laboratory, 2022.
-
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 nontrivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.
- Subjects :
- Stochastic Processes
uncertainty quantification
Bayesian inference
Uncertainty
Biomedical Engineering
Biophysics
Gene Expression
Chemical Master Equation
Bioengineering
Models, Biological
Biochemistry
Settore FIS/07 - Fisica Applicata(Beni Culturali, Ambientali, Biol.e Medicin)
Synthetic Likelihoods
Stochastic Modelling
Biomaterials
chemical master equation
Bayesian Inference
Uncertainty Quantification
synthetic likelihoods
stochastic modelling
Algorithms
Biotechnology
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Öcal, K, Gutmann, M U, Sanguinetti, G & Grima, R 2022, ' Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models ', Journal of the Royal Society. Interface, vol. 19, no. 192, 20220153 . https://doi.org/10.1098/rsif.2022.0153
- Accession number :
- edsair.doi.dedup.....15203e84febbfa0bda70a62b222f1382