  Developments in transcriptomics techniques have caused a large demand in
tailored computational methods for modelling gene expression dynamics from
experimental data. Recently, so-called single-cell experiments have
revolutionised genetic studies. These experiments yield gene expression data in
single cell resolution for a large number of cells at a time. However, the
cells are destroyed in the measurement process, and so the data consist of
snapshots of an ensemble evolving over time, instead of time series. The
problem studied in this article is how such data can be used in modelling gene
regulatory dynamics. Two different paradigms are studied for linear system
identification. The first is based on tracking the evolution of the
distribution of cells over time. The second is based on the so-called
pseudotime concept, identifying a common trajectory through the state space,
along which cells propagate with different rates. Therefore, at any given time,
the population contains cells in different stages of the trajectory. Resulting
methods are compared in numerical experiments.
