Sensitivity, robustness and identifiability in stochastic chemical kinetics models

TL;DR

This paper introduces a novel numerical method to compute Fisher Information Matrices for stochastic chemical kinetics models using the linear noise approximation, enabling sensitivity and identifiability analysis without Monte Carlo simulations.

Contribution

The authors develop the first efficient method to calculate Fisher Information for stochastic chemical kinetics models directly from differential equations, bypassing simulation-based approaches.

Findings

Significant differences between stochastic and deterministic models identified.

Variability and correlations impact parameter sensitivity and robustness.

Method facilitates experimental design for cellular stochastic processes.

Abstract

We present a novel and simple method to numerically calculate Fisher Information Matrices for stochastic chemical kinetics models. The linear noise approximation is used to derive model equations and a likelihood function which leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher Information Matrix to solving a set of ordinary differential equations. {This is the first method to compute Fisher Information for stochastic chemical kinetics models without the need for Monte Carlo simulations.} This methodology is then used to study sensitivity, robustness and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.