Hierarchical optimization for the efficient parametrization of ODE models

1 General Information

C Loos, S Krause, J Hasenauer (2013) : Hierarchical optimization for the efficient parametrization of ODE models Bioinformatics, Volume 34, Issue 24, Pages 4266–4273.

https://doi.org/10.1093/bioinformatics/bty514

1.1 Summary

In ODE-based modeling in the systems biology field, often only relative data is available whose measurement errors are not unknown. A common approach to deal with this setting is the introduction of scaling and noise parameters, see Lessons Learned from Quantitative Dynamical Modeling in Systems Biology. Since introducing additional parameters can decrease the performance of the parameter optimization algorithm, this paper introduced an hierarchical approach to separate the fitting of the *nuisance* from the *dynamic* parameters in every step. This was compared to the standard approach of fitting all parameters simultaneously in terms of optimizer convergence and computational efficiency.

1.2 Study outcomes

List the paper results concerning method comparison and benchmarking:

1.2.1 Outcome O1

The performance of ...

Outcome O1 is presented as Figure X in the original publication.

1.2.2 Outcome O2

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

1.2.3 Outcome On

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Outcome On is presented as Figure X in the original publication.

1.2.4 Further outcomes

If intended, you can add further outcomes here.

1.3 Study design and evidence level

1.3.1 General aspects

You can describe general design aspects here. The study designs for describing specific outcomes are listed in the following subsections:

1.3.2 Design for Outcome O1

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• Configuration parameters were chosen ...
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1.3.3 Design for Outcome O2

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1.3.4 Design for Outcome O

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1.5 References

The list of cited or related literature is placed here.