Difference between revisions of "Performance of objective functions and optimization procedures for parameter estimation in system biology models"
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| − | + | The provided claims are tested on 3 parameter estimation problems. | |
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| − | + | ==== Identifiability ==== | |
| + | Employing data-driven normalization instead of scaling factors improved the identifiability of dynamic parameters, providing a computational example to demonstrate how this occurs. | ||
==== Outcome O2 ==== | ==== Outcome O2 ==== | ||
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| − | Outcome O2 is presented as Figure X in the original publication. | + | Outcome O2 is presented as Figure X in the original publication. |
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=== Study design and evidence level === | === Study design and evidence level === | ||
Revision as of 12:44, 25 February 2020
Contents
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 Outcome O2
...
Outcome O2 is presented as Figure X in the original publication.
4 Study design and evidence level
4.1 General aspects
You can describe general design aspects here. The study designs for describing specific outcomes are listed in the following subsections:
4.2 Design for Outcome O1
- The outcome was generated for ...
- Configuration parameters were chosen ...
- ...
4.3 Design for Outcome O2
- The outcome was generated for ...
- Configuration parameters were chosen ...
- ...
...
4.4 Design for Outcome O
- The outcome was generated for ...
- Configuration parameters were chosen ...
- ...
5 Further comments and aspects
6 References
The list of cited or related literature is placed here.
