Difference between revisions of "Performance of objective functions and optimization procedures for parameter estimation in system biology models"

(Summary)
(Study outcomes)
Line 7: Line 7:
  
 
=== Study outcomes ===
 
=== Study outcomes ===
List the paper results concerning method comparison and benchmarking:
+
The provided claims are tested on 3 parameter estimation problems.
==== Outcome O1 ====
 
The performance of ...
 
  
Outcome O1 is presented as Figure X in the original publication.  
+
==== 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 ====
 
...
 
...
  
Outcome O2 is presented as Figure X in the original publication.  
+
Outcome O2 is presented as Figure X in the original publication.
 
==== Outcome On ====
 
...
 
 
 
Outcome On is presented as Figure X in the original publication.
 
 
 
==== Further outcomes ====
 
If intended, you can add further outcomes here.
 
 
 
  
 
=== Study design and evidence level ===
 
=== Study design and evidence level ===

Revision as of 12:44, 25 February 2020

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.