title: Analysis of CFSE time-series data using division-, age- and label-structured population models.
abstract: In vitro and in vivo cell proliferation is often studied using the dye carboxyfluorescein succinimidyl ester (CFSE). The CFSE time-series data provide information about the proliferation history of populations of cells. While the experimental procedures are well established and widely used, the analysis of CFSE time-series data is still challenging. Many available analysis tools do not account for cell age and employ optimization methods that are inefficient (or even unreliable).
  
  
  
  We present a new model-based analysis method for CFSE time-series data. This method uses a flexible description of proliferating cell populations, namely, a division-, age- and label-structured population model. Efficient maximum likelihood and Bayesian estimation algorithms are introduced to infer the model parameters and their uncertainties. These methods exploit the forward sensitivity equations of the underlying partial differential equation model for efficient and accurate gradient calculation, thereby improving computational efficiency and reliability compared with alternative approaches and accelerating uncertainty analysis. The performance of the method is assessed by studying a dataset for immune cell proliferation. This revealed the importance of different factors on the proliferation rates of individual cells. Among others, the predominate effect of cell age on the division rate is found, which was not revealed by available computational methods.
  
  
  
  The MATLAB source code implementing the models and algorithms is available from http://janhasenauer.github.io/ShAPE-DALSP/Contact: jan.hasenauer@helmholtz-muenchen.de
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany Department of Mathematical Modeling of Biological Systems, Center for Mathematics, Technische Universität München, Garching 85748, Germany.
authors.forename: Sabrina
authors.initials: S
authors.lastname: Hross
authors.affiliation: Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany Department of Mathematical Modeling of Biological Systems, Center for Mathematics, Technische Universität München, Garching 85748, Germany.
authors.forename: Jan
authors.initials: J
authors.lastname: Hasenauer
mesh_terms: D001499:Bayes Theorem; D049109:Cell Proliferation; D008962:Models, Theoretical; D015203:Reproducibility of Results