Identifiability analysis for stochastic differential equation models in systems biology
Abstract
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 ofparameter identifiabilityhave 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 assessstructural 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 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 onGithub.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 01, 2020
- Source ID
- 10.1098/rsif.2020.0652
Entities
People
- Alexander P Browning
- David J Warne
- Kevin Burrage
- Matthew Simpson
- Ruth Baker
Organizations
- Air Force Office of Scientific Research
- Australian Research Council
- Biotechnology and Biological Sciences Research Council
- Queensland University of Technology
- University of Oxford