基于多尺度随机反应网络混合逼近的随机滤波器
Stochastic filters based on hybrid approximations of multiscale stochastic reaction networks
摘要：
我们考虑的问题，估计的动态潜伏状态的细胞内多尺度随机反应网络的时间过程测量的荧光报告。我们首先证明，可以通过解决过滤问题的简化模型，表示作为一个混合动力过程的动态过滤问题，构建准确的解决方案。模型降阶是基于利用原始网络中的时间尺度分离，它可以大大减少模拟动力学所需的计算工作量。这使我们能够开发有效的粒子滤波器，以解决过滤问题的原始模型，通过应用粒子滤波器的简化模型。我们用一个数值例子说明了我们的方法的精度和计算效率。
英文摘要：
We consider the problem of estimating the dynamic latent states of an intracellular multiscale stochastic reaction network from time-course measurements of fluorescent reporters. We first prove that accurate solutions to the filtering problem can be constructed by solving the filtering problem for a reduced model that represents the dynamics as a hybrid process. The model reduction is based on exploiting the time-scale separations in the original network, and it can greatly reduce the computational effort required to simulate the dynamics. This enables us to develop efficient particle filters to solve the filtering problem for the original model by applying particle filters to the reduced model. We illustrate the accuracy and the computational efficiency of our approach using a numerical example.
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