Identifiability analysis for stochastic differential equation models in systems biology
Alexander P Browning1,2, David J Warne1,2, Kevin Burrage1,2,3,4
1School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
Journal of the Royal Society, Interface
|December 16, 2020
Summary
This study introduces parameter identifiability analysis for stochastic differential equation (SDE) models. It shows SDE models can yield more parameter information than deterministic models, improving predictive power and mechanistic insight.
Area of Science:
- Mathematical modeling
- Computational biology
- Systems biology
Background:
- Mathematical models are crucial for scientific research, but their parameter identifiability can be overlooked.
- Parameter identifiability is well-established for ordinary differential equation (ODE) models but not for stochastic models.
- Issues with parameter identifiability impact a model's predictive capabilities and mechanistic insights.
Purpose of the Study:
- To provide an accessible introduction to parameter identifiability analysis for stochastic models.
- To demonstrate applying ODE identifiability methods to stochastic differential equation (SDE) models.
- To assess the identifiability of parameters in SDE models using practical case studies.
Main Methods:
- Analysis of structural identifiability for ODEs describing statistical moments of the stochastic process.
- Application of open-source software tools for identifiability analysis.
- Utilizing synthetic data and Markov chain Monte Carlo (MCMC) methods to assess parameter identifiability.
Main Results:
- Demonstrated application of ODE identifiability analysis techniques to SDE models.
- Showcased that SDE models can often extract more information about parameters than deterministic models.
- Provided practical case studies illustrating the methods and findings.
Conclusions:
- Parameter identifiability analysis is applicable to SDE models, extending existing ODE methods.
- SDE models offer enhanced potential for parameter estimation compared to deterministic models.
- The study provides accessible methods and open-source code for SDE identifiability analysis.
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