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

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=== Citation ===
Andrea Degasperi, Dirk Fey & Boris N. Kholodenko, [https://www.nature.com/articles/s41540-017-0023-2.pdf Performance of objective functions and optimisation procedures for parameter estimation in system biology models], 2017, Systems Biology and Applications volume 3, Article number: 20

=== 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.

=== Study outcomes ===
The provided claims are tested on 3 parameter estimation problems.

==== 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 ====
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Outcome O2 is presented as Figure X in the original publication.

=== Study design and evidence level ===
==== General aspects ====
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The study designs for describing specific outcomes are listed in the following subsections:

==== Design for Outcome O1 ====
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==== Design for Outcome O2 ====
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==== Design for Outcome O ====
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=== Further comments and aspects ===

=== References ===
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