Experimental analysis and modeling of single-cell time-course data

Abstract

Contemporary single-cell experiments produce vast amounts of data, but the interpretation of these data is far from straightforward. In particular, understanding mechanisms and sources of cell-to-cell variability, given highly complex and nonlinear cellular networks, precludes intuitive interpretation. It requires careful computational and mathematical analysis instead. Here, we discuss different types of single-cell data and computational, model-based methods currently used to analyze them. We argue that mechanistic models incorporating subpopulation or cell-specific parameters can help to identify sources of variation and to understand experimentally observed behaviors. We highlight how data types and qualities, together with the nonlinearity of single-cell dynamics, make it challenging to identify the correct underlying biological mechanisms and we outline avenues to address these challenges.