add the reformulated version of Fiedler_BMCSystBiol2016

[m-philipps]

• closes issue Add reformulated Fiedler_BMCSystBiol2016 #252
• add reformulated version of the existing Fiedler_BMCSystBiol2016 model, taken from the same publication, that removes non-identifiabilities
• add a datasetId column for the existing Fiedler_BMCSystBiol2016 model

[FFroehlich]

would assume that all of the data is the same – why not just add separate model/parameters/yaml file to the base directory and reuse everything else?

[m-philipps]

The data is the same, the observable Ids changed (e.g. pErk -> pErk_rel) and that is propagated through the noise and observable parameters. Only the experimental condition files are the same.

I could rename the observables and associated parameters but I have a minor preference for keeping the new names to distinguish the interpretation of the observables in the old and reformulated model.

[dilpath]

Looks good. Since this is similar to submitting a new problem, you could add the checklist to this PR and indicate what is done/not done (of course, not everything has to be done since this is not the "main" problem -- simply leave those unchecked as a matter of record).

[m-philipps]

• The PEtab problem is based on a model that is peer-reviewed and published
• The problem ID is in the format {LAST_NAME_OF_FIRST_AUTHOR}_{ABBREVIATED_JOURNAL_NAME}{YEAR_OF_PUBLICATION}
  $\to$ No, Fiedler_BMCSystBiol206 already exists, ID is Fiedler_BMCSystBiol206_rel
• The problem ID is in the pull request title
• There is a GitHub issue for this problem
  • The problem ID is in the issue title
  • A brief model description (one or two sentences)
  • A brief data description (one or two sentences)
  • The issue and PR are linked to each other
  • Differences between the implementation and the original publication are described
  • Experience of fitting / uncertainty analysis (e.g. optimizer used, hyperparameters, reproducibility of best fit)
  • Source of nominal parameters (e.g.: taken from the original publication, or from your own fitting)
• The SBML file
  • Annotation with reference to the original publication (example)
  • The model ID and model name attributes in the SBML model file match the problem name (example)
• PEtab files
  • A "simulated data" measurement table is included, using the nominal parameters
  • A visualization table is included, that can be used with the simulated data to reproduce figures from the original publication
  • The PEtab problem is valid (check with e.g. petablint -vy problem.yaml)
• The PEtab problem author(s) are assigned to the GitHub issue
• The README has been updated with bmp-create-overview --update (requires pip install -e src/python/bmp from the repository root)
  • The new PEtab problem row in the generated table has the correct reference (and other entries)

[dilpath]

• Annotation with reference to the original publication (example)

I think this is missing, but also fine to uncheck this box instead.