Open Access Open Badges Research article

Reconstruction of cell population dynamics using CFSE

Andrew Yates1*, Cliburn Chan2*, Jessica Strid3, Simon Moon45, Robin Callard6, Andrew JT George7 and Jaroslav Stark45

Author Affiliations

1 Department of Biology, Emory University, 1510 Clifton Road, Atlanta, GA 30322, USA

2 Department of Biostatistics and Bioinformatics, Duke University Laboratory of Computational Immunology, 106 North Bldg, Research Drive, Box 90090, Durham, NC 27708, USA

3 Peter Gorer Department of Immunobiology, Guy's, King's and St Thomas' School of Medicine, King's College London, Guy's Hospital, London SE1 9RT, UK

4 Department of Mathematics, Imperial College London, 180 Queen's Gate, London SW7 2BZ, UK

5 Centre for Integrative Systems Biology at Imperial College (CISBIC), UK

6 Immunobiology Unit, Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK

7 Department of Immunology, Faculty of Medicine, Imperial College London, Hammersmith Hospital, London W12 0NN, UK

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BMC Bioinformatics 2007, 8:196  doi:10.1186/1471-2105-8-196

Published: 12 June 2007



Quantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division in vitro and in vivo and provides a rich source of information with which to test models of cell kinetics. Cell division and death have a stochastic component at the single-cell level, and the probabilities of these occurring in any given time interval may also undergo systematic variation at a population level. This gives rise to heterogeneity in proliferating cell populations. Branching processes provide a natural means of describing this behaviour.


We present a likelihood-based method for estimating the parameters of branching process models of cell kinetics using CFSE-labeling experiments, and demonstrate its validity using synthetic and experimental datasets. Performing inference and model comparison with real CFSE data presents some statistical problems and we suggest methods of dealing with them.


The approach we describe here can be used to recover the (potentially variable) division and death rates of any cell population for which division tracking information is available.