Practical identifiability in the frame of nonlinear mixed effects models: the example of the in vitro erythropoiesis - Ronan Duchesne; Anissa Guillemin; Olivier Gandrillon; Fabien Crauste (BMC Bioinformatics, 2021)
Abstract Background Nonlinear mixed effects models provide a way to mathematically describe experimental data involving a lot of inter-individual heterogeneity. In order to assess their practical identifiability and estimate confidence intervals for their parameters, most mixed effects modelling programs use the Fisher Information Matrix. However, in complex nonlinear models, this approach can mask practical unidentifiabilities. Results Herein we rather propose a multistart approach, and use it to simplify our model by reducing the number of its parameters, in order to make it identifiable. Our model describes several cell populations involved in the in vitro differentiation of chicken erythroid progenitors grown in the same environment. Inter-individual variability observed in cell population counts is explained by variations of the differentiation and proliferation rates between replicates of the experiment. Alternatively, we test a model with varying initial condition. Conclusions We conclude by relating experimental variability to precise and identifiable variations between the replicates of the experiment of some model parameters.