Inference-based assessment of parameter identifiability in nonlinear biological models. Issue 144 (18th July 2018)

• Record Type: Journal Article
• Authors: Daly, Aidan C.; Gavaghan, David; Cooper, Jonathan; Tavener, Simon
• Is Part Of: Journal of the Royal Society interface. Volume 15:Issue 144(2018)
• Publication Date: 2018-07-18
• Subjects: identifiability -- experimental design -- inverse sensitivity -- Markov chain Monte Carlo -- approximate Bayesian computation; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2018.0318
• Languages: English
• ISSNs: 1742-5689

Abstract

Abstract : As systems approaches to the development of biological models become more mature, attention is increasingly focusing on the problem of inferring parameter values within those models from experimental data. However, particularly for nonlinear models, it is not obvious, either from inspection of the model or from the experimental data, that the inverse problem of parameter fitting will have a unique solution, or even a non-unique solution that constrains the parameters to lie within a plausible physiological range. Where parameters cannot be constrained they are termed 'unidentifiable'. We focus on gaining insight into the causes of unidentifiability using inference-based methods, and compare a recently developed measure-theoretic approach to inverse sensitivity analysis to the popular Markov chain Monte Carlo and approximate Bayesian computation techniques for Bayesian inference. All three approaches map the uncertainty in quantities of interest in the output space to the probability of sets of parameters in the input space. The geometry of these sets demonstrates how unidentifiability can be caused by parameter compensation and provides an intuitive approach to inference-based experimental design.