RT Journal Article
SR Electronic
T1 Identifiability analysis for stochastic differential equation models in systems biology
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.10.245233
DO 10.1101/2020.08.10.245233
A1 Alexander P Browning
A1 David J Warne
A1 Kevin Burrage
A1 Ruth E Baker
A1 Matthew J Simpson
YR 2020
UL http://biorxiv.org/content/early/2020/10/15/2020.08.10.245233.abstract
AB Mathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Identifiability analysis is well-established for deterministic, ordinary differential equation (ODE) models, but there are no commonly-adopted methods for analysing identifiability in stochastic models. We provide an accessible introduction to identifiability analysis and demonstrate how existing ideas for analysis of ODE models can be applied to stochastic differential equation (SDE) models through four practical case studies. To assess structural identifiability, we study ODEs that describe the statistical moments of the stochastic process using open-source software tools. Using practically-motivated synthetic data and Markov-chain Monte Carlo (MCMC) methods, we assess parameter identifiability in the context of available data. Our analysis shows that SDE models can often extract more information about parameters than deterministic descriptions. All code used to perform the analysis is available on Github.Competing Interest StatementThe authors have declared no competing interest.