Benchmarking optimization methods for parameter estimation in large kinetic models

1 Paper name

Alejandro F Villaverde Fabian Fröhlich Daniel Weindl Jan Hasenauer Julio R Banga, Benchmarking optimization methods for parameter estimation in large kinetic models, Bioinformatics, Volume 35, Issue 5, 01 March 2019, Pages 830–838,

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1.1 Summary

Briefly describe the scope of the paper, i.e. the field of research and/or application.

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

...

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

1.2.3 Outcome On

...

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

The best performing method is introduced within this study. In general, presenting a new approach by comparing with existing ones easily leads to biased outcomes.

The authors provide source-code which seems to enable reproduction of the presented results. This is very valuable.

The study was jointly performed from experts of two fields: stochastic global and deterministic local methods to ensure a fair comparison.

The study designs for describing specific outcomes are listed in the following subsections:

seven previously published estimation problems

1.3.2 Design for Outcome O1

• The outcome was generated for ...
• Configuration parameters were chosen ...
• ...

1.3.3 Design for Outcome O2

• The outcome was generated for ...
• Configuration parameters were chosen ...
• ...

...

1.3.4 Design for Outcome O

• The outcome was generated for ...
• Configuration parameters were chosen ...
• ...

1.4 Further comments and aspects

• The authors provide
• A

1.5 References

The list of cited or related literature is placed here.