Optimal Observation Time Points in Stochastic Chemical Kinetics

TL;DR

This paper introduces a numerical method to identify optimal observation times in stochastic chemical kinetics experiments, enhancing information gain while reducing costs in single-cell biological measurements.

Contribution

It presents a novel numerical scheme to approximate Fisher information for planning optimal observation times in stochastic biochemical models.

Findings

Effective identification of optimal sampling times.

Application to case studies demonstrating improved experimental design.

Potential to reduce costs and increase data quality in biological experiments.

Abstract

Wet-lab experiments, in which the dynamics within living cells are observed, are usually costly and time consuming. This is particularly true if single-cell measurements are obtained using experimental techniques such as flow-cytometry or fluorescence microscopy. It is therefore important to optimize experiments with respect to the information they provide about the system. In this paper we make a priori predictions of the amount of information that can be obtained from measurements. We focus on the case where the measurements are made to estimate parameters of a stochastic model of the underlying biochemical reactions. We propose a numerical scheme to approximate the Fisher information of future experiments at different observation time points and determine optimal observation time points. To illustrate the usefulness of our approach, we apply our method to two interesting case studies.