论文标题
随机化学系统的多尺度全球灵敏度分析
Multiscale global sensitivity analysis for stochastic chemical systems
论文作者
论文摘要
敏感性分析通常在简化的替代模型上进行，因为对原始模型的这种分析的成本可能会令人望而却步。一般而言，人们对敏感性结果的诱导偏差鲜为人知。在化学动力学的框架内，在基于方差的方法的情况下，我们提供了上述方法的全部理由，提供原始模型通过热力学极限的替代模型结果。我们还提供了Michaelis-Menteren系统和描述遗传振荡器的生化反应网络的说明性数值示例。
Sensitivity analysis is routinely performed on simplified surrogate models as the cost of such analysis on the original model may be prohibitive. Little is known in general about the induced bias on the sensitivity results. Within the framework of chemical kinetics, we provide a full justification of the above approach in the case of variance based methods provided the surrogate model results from the original one through the thermodynamic limit. We also provide illustrative numerical examples in context of a Michaelis--Menten system and a biochemical reaction network describing a genetic oscillator.
