Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.
Identifiability
Model-based design of experiments
Practical identifiability
Profile likelihood
Journal
Bulletin of mathematical biology
ISSN: 1522-9602
Titre abrégé: Bull Math Biol
Pays: United States
ID NLM: 0401404
Informations de publication
Date de publication:
08 May 2024
08 May 2024
Historique:
received:
09
01
2024
accepted:
23
04
2024
medline:
8
5
2024
pubmed:
8
5
2024
entrez:
8
5
2024
Statut:
epublish
Résumé
Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.
Identifiants
pubmed: 38717656
doi: 10.1007/s11538-024-01304-1
pii: 10.1007/s11538-024-01304-1
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
70Subventions
Organisme : University of Canterbury
ID : Doctoral Scholarship
Informations de copyright
© 2024. The Author(s).
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