高维随机反应网络的加速敏感性分析
Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks
摘要：
在本文中，提出了一个两步的战略参数敏感性分析，这样的系统，利用最近提出的随机动力学的敏感性分析方法之间的优势和协同作用。第一种方法通过Fisher信息矩阵对轨迹的基本分布进行随机动力学的敏感性分析;第二种方法是一种减少方差、有限差分、梯度型敏感性方法，依赖于用于方差减少的随机耦合技术。在这里，我们证明了这两种方法可以结合起来，部署在一起，通过一个新的灵敏度界限，其中包括感兴趣的数量的方差以及从第一种方法估计的Fisher信息矩阵。所提出的策略的第一步标签的敏感性使用的界限和筛选出的不敏感的参数在一个受控的方式也基于新的敏感性界限。在所提出的策略的第二步中，有限差分法仅适用于在第一步中没有被筛选出的（潜在的）敏感参数的灵敏度估计。在一个含有50个参数的表皮生长因子网络和一个含有80个参数的蛋白质稳态网络上的实验结果表明，该策略能够快速发现和丢弃不敏感的参数，并在剩余的潜在敏感参数中准确地估计敏感性。新的灵敏度策略可以比测试所有参数的当前最先进方法快几倍，特别是在“草率”的系统中。特别地，计算加速通过参数的总数与敏感参数的数量之间的比率来量化。
英文摘要：
In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner based also on the new sensitivity bound. In the second step of the proposed strategy, the finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the number of the sensitive parameters.
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