通过轨迹重加权的随机建模中的稳态参数灵敏度
Steady-state parameter sensitivity in stochastic modeling via trajectory reweighting
摘要：
参数敏感性分析是生化网络模型建立和分析的有力工具。对于随机模拟，参数灵敏度分析可能在计算上是昂贵的，需要对参数的扰动值进行多次模拟。在这里，我们使用轨迹重新加权来推导出一种方法，用于计算随机模拟中的灵敏度系数，而不显式地扰动参数值，避免了重复模拟的需要。该方法允许同时计算多个灵敏度系数。我们的方法恢复了最初通过应用随机过程一般理论中的Girsanov测度变换获得的结果[A. Plyasidae和A. P.Arkin，J.Comp.Phys.221，724（2007）]。我们建立在这些结果，以显示该方法可以用来计算稳态灵敏度系数从一个单一的模拟运行，我们提出了各种效率的提高。对于生物化学信号网络的模型，该方法具有特别简单的实现。我们展示了它的应用程序的信号网络显示随机聚焦和遗传开关，并提出了精确的结果与线性倾向函数的模型。
英文摘要：
Parameter sensitivity analysis is a powerful tool in the building and analysis of biochemical network models. For stochastic simulations, parameter sensitivity analysis can be computationally expensive, requiring multiple simulations for perturbed values of the parameters. Here, we use trajectory reweighting to derive a method for computing sensitivity coefficients in stochastic simulations without explicitly perturbing the parameter values, avoiding the need for repeated simulations. The method allows the simultaneous computation of multiple sensitivity coefficients. Our approach recovers results originally obtained by application of the Girsanov measure transform in the general theory of stochastic processes [A. Plyasunov and A. P. Arkin, J. Comp. Phys. 221, 724 (2007)]. We build on these results to show how the method can be used to compute steady-state sensitivity coefficients from a single simulation run, and we present various efficiency improvements. For models of biochemical signaling networks the method has a particularly simple implementation. We demonstrate its application to a signaling network showing stochastic focussing and to a bistable genetic switch, and present exact results for models with linear propensity functions.
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