Efficient inference and identifiability analysis for differential equation models with random parameters.

ABSTRACT: Heterogeneity is a dominant factor in the behaviour of many biological processes. Despite this, it is common for mathematical and statistical analyses to ignore biological heterogeneity as a source of variability in experimental data. Therefore, methods for exploring the identifiability of models that explicitly incorporate heterogeneity through variability in model parameters are relatively underdeveloped. We develop a new likelihood-based framework, based on moment matching, for inference and identifiability analysis of differential equation models that capture biological heterogeneity through parameters that vary according to probability distributions. As our novel method is based on an approximate likelihood function, it is highly flexible; we demonstrate identifiability analysis using

SUBMITTER: Browning AP 

PROVIDER: S-EPMC9731444 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature