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Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay

Gregoire Pau12, Thomas Walter1345, Beate Neumann1, Jean-Karim Hériché1, Jan Ellenberg1 and Wolfgang Huber1*

Author Affiliations

1 EMBL, Heidelberg, 69117 Germany

2 Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, California, 94080, USA

3 Centre for Computational Biology, Mines ParisTech, Fontainebleau, 77300 France

4 Institut Curie, Paris, 75248, France

5 U900, INSERM, Paris, 75248, France

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BMC Bioinformatics 2013, 14:308  doi:10.1186/1471-2105-14-308

Published: 16 October 2013



The combination of time-lapse imaging of live cells with high-throughput perturbation assays is a powerful tool for genetics and cell biology. The Mitocheck project employed this technique to associate thousands of genes with transient biological phenotypes in cell division, cell death and migration. The original analysis of these data proceeded by assigning nuclear morphologies to cells at each time-point using automated image classification, followed by description of population frequencies and temporal distribution of cellular states through event-order maps. One of the choices made by that analysis was not to rely on temporal tracking of the individual cells, due to the relatively low image sampling frequency, and to focus on effects that could be discerned from population-level behaviour.


Here, we present a variation of this approach that employs explicit modelling by dynamic differential equations of the cellular state populations. Model fitting to the time course data allowed reliable estimation of the penetrance and time of appearance of four types of disruption of the cell cycle: quiescence, mitotic arrest, polynucleation and cell death. Model parameters yielded estimates of the duration of the interphase and mitosis phases. We identified 2190 siRNAs that induced a disruption of the cell cycle at reproducible times, or increased the durations of the interphase or mitosis phases.


We quantified the dynamic effects of the siRNAs and compiled them as a resource that can be used to characterize the role of their target genes in cell death, mitosis and cell cycle regulation. The described population-based modelling method might be applicable to other large-scale cell-based assays with temporal readout when only population-level measures are available.

High-throughput time-lapse imaging assay; siRNA screening; Mitosis regulation; Differential equation modelling