Identification of biological models from single-cell data: a comparison between mixed-effects and moment-based inference

Experimental techniques in biology such as microfluidic devices and time-lapse microscopy allow tracking of the gene expression in single cells over time. So far, few attempts have been made to fully exploit these data for modeling the dynamics of biological networks in cell population. In this paper we compare two modeling approaches capable to describe cell-to-cell variability: Mixed-Effects (ME) models and the Chemical Master Equation (CME). We discuss how network parameters can be identified from experimental data and use real data of the HOG pathway in yeast to assess model quality. For CME we rely on the identification approach proposed in (Zechner et al., 2012) based on moments of the probability distribution involved in the CME. ME and moment-based inference (MB) will be also contrasted in terms of general features and possible uses in biology