Efficient parameter sensitivity computation for spatially-extended reaction networks

TL;DR

This paper introduces an efficient method for computing parameter sensitivities in stochastic spatial reaction-diffusion models, combining finite difference schemes with variance reduction and adaptive simulation techniques.

Contribution

It presents a novel hybrid approach that dynamically selects the best simulation method for spatially-extended reaction networks, improving efficiency and accuracy.

Findings

The method effectively reduces computational cost in sensitivity analysis.

Performance depends on network dynamics and chosen summary statistics.

The approach is validated across various scenarios.

Abstract

Reaction-diffusion models are widely used to study spatially-extended chemical reaction systems. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work, we focus on stochastic models of spatially-extended chemical reaction systems that involve partitioning the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially-extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially-extended network. We show that algorithmic performance depends on the dynamics of the given network and the choice of summary statistics. We then describe a hybrid technique that dynamically chooses the most appropriate simulation method for the network of interest. Our method is tested for functionality and accuracy in a range of different scenarios.