Evidence map›Paper›PMID 34413387›Full record

ArticleScientific reports2021

Assessing the role of initial conditions in the local structural identifiability of large dynamic models.

Dominique Joubert, J D Stigter, Jaap Molenaar

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Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 · Its place in the literature

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3 citing papers in PubMed.

  1. Article
  2. Article
  3. Algebra, Geometry and Topology of ERK Kinetics.Bulletin of mathematical biology · 2022
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Dominique JoubertBiometris, Department of Mathematical and Statistical Methods, Wageningen University and Research, 6708 PD, Wageningen, The Netherlands. dominique.joubert@wur.nl.
J D StigterBiometris, Department of Mathematical and Statistical Methods, Wageningen University and Research, 6708 PD, Wageningen, The Netherlands.
Jaap MolenaarBiometris, Department of Mathematical and Statistical Methods, Wageningen University and Research, 6708 PD, Wageningen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural identifiability is a binary property that determines whether or not unique parameter values can, in principle, be estimated from error-free input-output data. The many papers that have been written on this topic collectively stress the importance of this a priori analysis in the model development process. The story however, often ends with a structurally unidentifiable model. This may leave a model developer with no plan of action on how to address this potential issue. We continue this model exploration journey by identifying one of the possible sources of a model's unidentifiability: problematic initial conditions. It is well-known that certain initial values may result in the loss of local structural identifiability. Nevertheless, literature on this topic has been limited to the analysis of small toy models. Here, we present a systematic approach to detect problematic initial conditions of real-world systems biology models, that are usually not small. A model's identifiability can often be reinstated by changing the value of such problematic initial conditions. This provides modellers an option to resolve the "unidentifiable model" problem. Additionally, a good understanding of which initial values should rather be avoided can be very useful during experimental design. We show how our approach works in practice by applying it to five models. First, two small benchmark models are studied to get the reader acquainted with the method. The first one shows the effect of a zero-valued problematic initial condition. The second one illustrates that the approach also yields correct results in the presence of input signals and that problematic initial conditions need not be zero-values. For the remaining three examples, we set out to identify key initial values which may result in the structural unidentifiability. The third and fourth examples involve a systems biology Epo receptor model and a JAK/STAT model, respectively. In the final Pharmacokinetics model, of which its global structural identifiability has only recently been confirmed, we indicate that there are still sets of initial values for which this property does not hold.

Identifiers

PMID34413387
PMCPMC8376890

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