Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling.

Pauline Mazzocco 1, 2 Samuel Bernard 2, 3 Laurent Pujo-Menjouet 1, 2, 3
2 DRACULA - Multi-scale modelling of cell dynamics : application to hematopoiesis
CGMC - Centre de génétique moléculaire et cellulaire, Inria Grenoble - Rhône-Alpes, ICJ - Institut Camille Jordan [Villeurbanne], UCBL - Université Claude Bernard Lyon 1 : EA
Abstract : Carboxyfluorescein diacetate succinimidyl ester (CFSE) labelling has been widely used to track and study cell proliferation. Here we use mathematical modelling to describe the kinetics of immune cell proliferation after an in vitro polyclonal stimulation tracked with CFSE. This approach allows us to estimate a set of key parameters, including ones related to cell death and proliferation. We develop a three-phase model that distinguishes a latency phase, accounting for non-divided cell behaviour, a resting phase and the active phase of the division process. Parameter estimates are derived from model results, and numerical simulations are then compared to the dynamics of in vitro experiments, with different biological assumptions tested. Our model allows us to compare the dynamics of CD4+ and CD8+ cells, and to highlight their kinetic differences. Finally we perform a sensitivity analysis to quantify the impact of each parameter on proliferation kinetics. Interestingly, we find that parameter sensitivity varies with time and with cell generation. Our approach can help biologists to understand cell proliferation mechanisms and to identify potential pathological division processes.
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PLoS ONE, Public Library of Science, 2017, 12 (6), pp.e0179768
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https://hal.archives-ouvertes.fr/hal-01561667
Contributeur : Samuel Bernard <>
Soumis le : jeudi 13 juillet 2017 - 13:32:17
Dernière modification le : mercredi 19 juillet 2017 - 21:27:07

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  • HAL Id : hal-01561667, version 1
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Pauline Mazzocco, Samuel Bernard, Laurent Pujo-Menjouet. Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling.. PLoS ONE, Public Library of Science, 2017, 12 (6), pp.e0179768. <hal-01561667>

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