PEtab
PEtab - interoperable specification of parameter estimation problems in systems biology. Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been -- so far -- no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies. Specifications of PEtab, the PEtab Python library, as well as links to examples, and all supporting software tools are available at this https URL, a snapshot is available at this https URL. All original content is available under permissive licenses.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
Sorted by year (- Jakob Vanhoefer, Marta R. A. Matos, Dilan Pathirana, Yannik Schälte, Jan Hasenauer: yaml2sbml: Human-readable and -writable specification of ODE models and their conversion to SBML (2021) not zbMATH
- Leonard Schmiester, Yannik Schälte, Frank T. Bergmann, Tacio Camba, Erika Dudkin, Janine Egert, Fabian Fröhlich, Lara Fuhrmann, Adrian L. Hauber, Svenja Kemmer, Polina Lakrisenko, Carolin Loos, Simon Merkt, Wolfgang Müller, Dilan Pathirana, Elba Raimúndez, Lukas Refisch, Marcus Rosenblatt, Paul L. Stapor, Philipp Städter, Dantong Wang, Franz-Georg Wieland, Julio R. Banga, Jens Timmer, Alejandro F. Villaverde, Sven Sahle, Clemens Kreutz, Jan Hasenauer, Daniel Weindl: PEtab - interoperable specification of parameter estimation problems in systems biology (2020) arXiv
- Paul F. Lang, Sungho Shin, Victor M. Zavala: SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization (2020) arXiv
- Schmiester, Leonard; Weindl, Daniel; Hasenauer, Jan: Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach (2020)