Performance of objective functions and optimization procedures for parameter estimation in system biology models

Revision as of 13:19, 25 February 2020 by Bwday (talk | contribs) (Study design and evidence level)

1 Citation

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

2 Summary

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.

3.1 Identifiability

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

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.

6 References

The list of cited or related literature is placed here.