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

TL;DR

This paper introduces a profile likelihood-based method for reliable confidence intervals and observability analysis of dynamic biochemical models, effectively handling high-dimensional parameter spaces and non-identifiable parameters.

Contribution

It presents a novel approach using prediction profile likelihood for confidence intervals and observability analysis in complex dynamic models, especially in biological systems.

Findings

Prediction profile likelihood yields reliable confidence intervals.

The method simplifies high-dimensional parameter analysis to one-dimensional prediction spaces.

Applicable to models with non-identifiable parameters and noisy validation data.

Abstract

Mechanistic dynamic models of biochemical networks such as Ordinary Differential Equations (ODEs) contain unknown parameters like the reaction rate constants and the initial concentrations of the compounds. The large number of parameters as well as their nonlinear impact on the model responses hamper the determination of confidence regions for parameter estimates. At the same time, classical approaches translating the uncertainty of the parameters into confidence intervals for model predictions are hardly feasible. In this article it is shown that a so-called prediction profile likelihood yields reliable confidence intervals for model predictions, despite arbitrarily complex and high-dimensional shapes of the confidence regions for the estimated parameters. Prediction confidence intervals of the dynamic states allow a data-based observability analysis. The approach renders the issue of sampling a high-dimensional parameter space into evaluating one-dimensional prediction spaces. The method is also applicable if there are non-identifiable parameters yielding to some insufficiently specified model predictions that can be interpreted as non-observability. Moreover, a validation profile likelihood is introduced that should be applied when noisy validation experiments are to be interpreted. The properties and applicability of the prediction and validation profile likelihood approaches are demonstrated by two examples, a small and instructive ODE model describing two consecutive reactions, and a realistic ODE model for the MAP kinase signal transduction pathway. The presented general approach constitutes a concept for observability analysis and for generating reliable confidence intervals of model predictions, not only, but especially suitable for mathematical models of biological systems.