Stochastic filters based on hybrid approximations of multiscale stochastic reaction networks

TL;DR

This paper introduces a hybrid approximation-based filtering method for multiscale stochastic reaction networks, significantly reducing computational costs while maintaining accuracy in estimating intracellular dynamic states.

Contribution

It develops a hybrid model reduction technique leveraging time-scale separation to enable efficient particle filtering for complex stochastic reaction networks.

Findings

The hybrid model accurately captures the dynamics of multiscale networks.

The proposed filtering method reduces computational effort substantially.

Numerical examples demonstrate the approach's efficiency and accuracy.

Abstract

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.