Performance of objective functions and optimization procedures for parameter estimation in system biology models
Andrea Degasperi, Dirk Fey & Boris N. Kholodenko, Performance of objective functions and optimisation procedures for parameter estimation in system biology models, 2017, Systems Biology and Applications volume 3, Article number: 20
In systems biology, relative data are a common occurrence. In ODE-based models, this is regarded by either introducing scaling parameters or data-driven normalization to bring data and simulations onto the same scale. It was shown in this article, that data-driven normalization improves optimization performance and does not aggravate non-identifiability problems compared to a scaling factor approach. Furthermore, this article reports that hybrid optimization methods which combine stochastic global and deterministic local search outperforms deterministic local gradient-based strategies.
3 Study outcomes
The provided claims are tested on 3 parameter estimation problems.
Employing data-driven normalization instead of scaling factors improved the identifiability of dynamic parameters, providing a computational example to demonstrate how this occurs.
3.2 Convergence Speed
As visualized in Fig. 4 and Fig. 5 of the original publication, convergence speed was consistently improved using data driven normalization compared to scaling factors. Combining the data-driven normalization with the hybrid optimization algorithm GLSDC provided the best performance results especially in high-parameter settings.
4 Study design and evidence level
- Although the previously best-performing method using LSQNONLIN with sensitivity equations as found in Lessons Learned from Quantitative Dynamical Modeling in Systems Biology has been mentioned, but a comparison with GLSDC was restricted to use of their implementations of it.
- The study used Least-Squares instead of Likelihood as objective function, omitting error model fits.
- The notion of practical identifiability does not coincide with other literature, see for example e.g. Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood
5 Further comments and aspects
- Additionally to the performance advantages of not using scaling factors, it is also stated that the amount of overfitting is reduced.
The list of cited or related literature is placed here.