论文标题
基于多尺度随机反应网络混合近似的随机过滤器
Stochastic filters based on hybrid approximations of multiscale stochastic reaction networks
论文作者
论文摘要
我们考虑了通过荧光记者的时间顺序测量的细胞内多尺度随机反应网络的动态潜在状态的问题。我们首先证明，可以通过解决将动力学作为混合过程的简化模型解决过滤问题来构建过滤问题的准确解决方案。减少模型是基于利用原始网络中的时间尺度分离的基础，并且可以大大减少模拟动力学所需的计算工作。这使我们能够开发有效的粒子过滤器来通过将粒子过滤器应用于还原模型来解决原始模型的过滤问题。我们使用数值示例说明了方法的准确性和计算效率。
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.
