空间扩展反应网络参数灵敏度的高效计算
Efficient parameter sensitivity computation for spatially-extended reaction networks
摘要：
反应扩散模型是研究空间扩展化学反应系统的常用模型。为了了解反应扩散模型的动力学如何受到其输入参数的影响，需要计算参数灵敏度的有效方法。在这项工作中，我们专注于随机模型的空间扩展的化学反应系统，涉及分区的计算域成体素。参数灵敏度通常使用Monte Carlo技术计算，该技术通常在计算上是昂贵的;然而，方差减少技术可以减少所需的Monte Carlo模拟的数量。通过利用空间扩展的反应网络的特征动力学，我们能够适应现有的有限差分格式来鲁棒地估计空间扩展的网络中的参数灵敏度。我们表明，算法的性能取决于给定网络的动态和汇总统计量的选择。然后，我们描述了一种混合技术，动态选择最合适的模拟方法的网络的兴趣。我们的方法在一系列不同的场景中进行了功能和准确性测试。
英文摘要：
Reaction-diffusion models are widely used to study spatially-extended chemical reaction systems. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work, we focus on stochastic models of spatially-extended chemical reaction systems that involve partitioning the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially-extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially-extended network. We show that algorithmic performance depends on the dynamics of the given network and the choice of summary statistics. We then describe a hybrid technique that dynamically chooses the most appropriate simulation method for the network of interest. Our method is tested for functionality and accuracy in a range of different scenarios.
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