Identifying stochastic biochemical networks from single-cell population experiments: A comparison of approaches based on the Fisher information

In biochemical reaction networks stochasticity arising from molecular fluctuations often plays an important role. Recent years have seen an increasing number of studies which employed single-cell population experiments and used the measured stochasticity to estimate the parameters of stochastic kinetic models. Currently, there exist two approaches for extracting information about the model parameters from the observed variability. One uses the complete distribution of the measured population and is usually computationally very expensive, whereas the other uses only low-order moments and thereby sacrifices a part of the information in order to reduce the computational cost. Here, using three qualitatively different benchmark models, we investigate on the one hand how much information can be gained by exploiting the single-cell resolution of population experiments and on the other hand how much information is lost if only low-order moments of the measured distributions are considered. Our study allows one to better understand the advantage of single-cell population experiments and to gain insights into when and where moment-based parameter inference methods for their analysis are appropriate.