School of Informatics, University of Edinburgh, Edinburgh EH9 3JH, UK.
School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK.
J R Soc Interface. 2022 Jul;19(192):20220153. doi: 10.1098/rsif.2022.0153. Epub 2022 Jul 13.
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.
估计模型预测的不确定性是定量生物学的一项核心任务。单细胞水平的生物模型本质上是随机和非线性的，这给它们的统计估计带来了巨大的挑战，而统计估计不可避免地必须依赖于为了可处理性而牺牲准确性的近似方法。尽管人们对此非常感兴趣，但这种权衡的最佳点尚未找到。我们提出了一种用于描述包括反馈在内的随机基因表达的广泛的反应网络的不确定性量化的灵活方法。该方法基于从模拟中学习的可处理的粗粒化，即 ，来近似似然函数。我们证明，在许多非平凡的系统和数据集上，合成模型可以大大优于最先进的方法，为基因表达的随机模型的不确定性量化提供了一种准确且计算上可行的解决方案。
