Difference between revisions of "Benchmark problems for dynamic modeling of intracellular processes"

(Summary)
(Outcome O2)
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The comparison of trust-region-reflective and interior-point algorithms revealed that the former is better suited for most parameter estimation problems encountered in systems biology.
 
The comparison of trust-region-reflective and interior-point algorithms revealed that the former is better suited for most parameter estimation problems encountered in systems biology.
  
Outcome O2 is presented as Figure X in the original publication.  
+
Outcome O2 is presented as Figure 3 in the original publication.
+
 
 
==== Outcome O3 ====
 
==== Outcome O3 ====
 
The scaling behavior confirmed theoretical results showing that the number of optimizer steps does not depend on the number of model parameters.
 
The scaling behavior confirmed theoretical results showing that the number of optimizer steps does not depend on the number of model parameters.

Revision as of 14:43, 25 February 2020


1 Citation

Hass, Helge, et al. "Benchmark problems for dynamic modeling of intracellular processes." Bioinformatics 35.17 (2019): 3073-3082.

Permanent link to the paper


2 Summary

In this paper, a collection of ODE models with publicly available experimental data is compiled.

To prove its usefulness for computational studies within the field of ODE modeling, simulation studies are conducted to find the following three Outcomes which are connected to method comparison and performance assessment.

3 Study outcomes

3.1 Outcome O1

Optimization benefits from log-transformed parameter space. This could be due to increased convexity through log transformation.

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

3.2 Outcome O2

The comparison of trust-region-reflective and interior-point algorithms revealed that the former is better suited for most parameter estimation problems encountered in systems biology.

Outcome O2 is presented as Figure 3 in the original publication.

3.3 Outcome O3

The scaling behavior confirmed theoretical results showing that the number of optimizer steps does not depend on the number of model parameters.

Outcome O3 is presented as Figure X in the original publication.

3.4 Further outcomes

If intended, you can add further outcomes here.

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.