Uncertainty analysis
The software implements the profile likelihood approach
- Raue A., et al. Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics 25(15), 1923-1929, 2009.
This general approach allows to infer both the structural and the practical identifiability of parameters in non-linear and possibly dynamic models by calculating the profile likelihood. Furthermore, it can be used to calculate likelihood-based confidence intervals and to design optimal experiments that improve parameter identification and therefore also the predictability of a model.
An application to a model from cell biology (Becker et al., Science 2010, see in [example applications](Example applications)) that illustrates the iterative cycle between modeling and experimentation can be found in:
- Raue A., et al. Identifiability and Observability Analysis for Experimental Design in Non-Linear Dynamical Models. Chaos 20(4), 045105, 2010.
A more general overview about identifiability and its consequences on model predictions in terms of observability can be found in:
- Raue A., et al. Addressing Parameter Identifiability by Model-Based Experimentation. IET Systems Biology 5(2), 120-130, 2011.
The results obtained by the profile likelihood approach were compared to results of Markov-chain Monte Carlo sampling in:
- Raue A., et al. Joining Forces of Bayesian and Frequentist Methodology: A Study for Inference in the Presence of Non-Identifiability. Phil. Trans. Roy. Soc. A 371, 20110544, 2013.
- Hug S., et al. High-Dimensional Bayesian Parameter Estimation: Case Study for a Model of JAK2/STAT5 Signaling. Mathematical Biosciences, in press, 2013.
The profile likelihood approach was extended to cover arbitrary model predictions in:
- Kreutz C., et al. Likelihood based observability analysis and confidence intervals for predictions of dynamic models. BMC Systems Biology 6, 120, 2012.
A general overview about the profile likelihood methodology is given in:
- Kreutz C., et al. Profile Likelihood in Systems Biology. FEBS Journal 280(11), 2564-2571, 2013.