English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Mechanistic dynamic models of biochemical networks such as OrdinaryDifferential Equations (ODEs) contain unknown parameters like the reaction rateconstants and the initial concentrations of the compounds. The large number ofparameters as well as their nonlinear impact on the model responses hamper thedetermination of confidence regions for parameter estimates. At the same time,classical approaches translating the uncertainty of the parameters intoconfidence intervals for model predictions are hardly feasible.  In this article it is shown that a so-called prediction profile likelihoodyields reliable confidence intervals for model predictions, despite arbitrarilycomplex and high-dimensional shapes of the confidence regions for the estimatedparameters. Prediction confidence intervals of the dynamic states allow adata-based observability analysis. The approach renders the issue of sampling ahigh-dimensional parameter space into evaluating one-dimensional predictionspaces. The method is also applicable if there are non-identifiable parametersyielding to some insufficiently specified model predictions that can beinterpreted as non-observability. Moreover, a validation profile likelihood isintroduced that should be applied when noisy validation experiments are to beinterpreted.  The properties and applicability of the prediction and validation profilelikelihood approaches are demonstrated by two examples, a small and instructiveODE model describing two consecutive reactions, and a realistic ODE model forthe MAP kinase signal transduction pathway. The presented general approachconstitutes a concept for observability analysis and for generating reliableconfidence intervals of model predictions, not only, but especially suitablefor mathematical models of biological systems.

Likelihood based observability analysis and confidence intervals for predictions of dynamic models