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Developments in transcriptomics techniques have caused a large demand intailored computational methods for modelling gene expression dynamics fromexperimental data. Recently, so-called single-cell experiments haverevolutionised genetic studies. These experiments yield gene expression data insingle cell resolution for a large number of cells at a time. However, thecells are destroyed in the measurement process, and so the data consist ofsnapshots of an ensemble evolving over time, instead of time series. Theproblem studied in this article is how such data can be used in modelling generegulatory dynamics. Two different paradigms are studied for linear systemidentification. The first is based on tracking the evolution of thedistribution of cells over time. The second is based on the so-calledpseudotime 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. Resultingmethods are compared in numerical experiments.

Linear system identification from ensemble snapshot observations