ArticleBMC bioinformatics2021
Practical identifiability in the frame of nonlinear mixed effects models: the example of the in vitro erythropoiesis.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- One model to rule them all: Unification of voltage-gated potassium channel models via deep non-linear mixed effects modelling.PLoS computational biology · 2026Article
- A Nonparametric Approach to Practical Identifiability of Nonlinear Mixed Effects Models.Bulletin of mathematical biology · 2026Article
- In silico modeling of directed differentiation of induced pluripotent stem cells to definitive endoderm.PLoS computational biology · 2025Article
- Fitting and comparison of calcium-calmodulin kinetic schemes to a common data set using non-linear mixed effects modelling.PloS one · 2025Article
- In silico model development and optimization of in vitro lung cell population growth.PloS one · 2024Article
- Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Long-term durability of immune responses to the BNT162b2 and mRNA-1273 vaccines based on dosage, age and sex.Scientific reports · 2022Article
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Authors and funding
4 authors.
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Abstract
backgroundNonlinear 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.
resultsHerein 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.
conclusionsWe conclude by relating experimental variability to precise and identifiable variations between the replicates of the experiment of some model parameters.
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