A collection of mathematical models with experimental data in the PEtab format as benchmark problems in order to evaluate new and existing methodologies for data-based modelling. The publication for the introduction and analysis of the benchmark problem collection is available at https://academic.oup.com/bioinformatics/article/35/17/3073/5280731.

Contributions to the collection are very welcome. Please see CONTRIBUTING.md for more details.

Overview

The collection spans a wide variety of problems, in terms of application as well as the numbers of observables, parameters, measurements, and experimental conditions, and features such as events, preequilibration, and noise and prior distributions. For these details, see the sortable and filterable overview tables.

The following table lists the problems in the collection and the available encodings in PEtab v1 and v2. The PEtab files are available in the respective subdirectories.

ID	PEtab v1	PEtab v2	Description
Alkan_SciSignal2018	v1		Reaction network model of the DNA damage response and cell fate in cancer cells treated with chemotherapeutics, fitted to signaling and cell count data.
Armistead_CellDeathDis2024	v1		Small reaction network model of sphingolipid metabolism with ceramide feedback, fitted to lipidomics data of wild-type and mutant zebrafish embryos.
Bachmann_MSB2011	v1		Reaction network model of Epo-induced JAK2/STAT5 signaling with two negative feedback regulators (SOCS3, CIS) in murine erythroid progenitor cells.
Beer_MolBioSystems2014	v1		Small kinetic model of bacterial growth and production of the blue pigment indigoidine by engineered variants of a non-ribosomal peptide synthetase.
Bertozzi_PNAS2020	v1		SIR compartmental epidemiological model of the early COVID-19 outbreak in New York and California.
Blasi_CellSystems2016	v1		Reaction network model of combinatorial histone H4 acetylation states generated by motif-specific acetylation reactions.
Boehm_JProteomeRes2014	v1	v2	Reaction network model of Epo-induced STAT5A/STAT5B phosphorylation and homo- and heterodimerization, fitted to quantitative mass spectrometry data.
Borghans_BiophysChem1997	v1		Three-variable ODE model of complex calcium oscillations with Ca2+-activated IP3 degradation, fitted to measured calcium bursting in a single hepatocyte.
Brannmark_JBC2010	v1		Reaction network model of insulin receptor and IRS1 phosphorylation with receptor endocytosis and feedback in primary human adipocytes.
Bruno_JExpBot2016	v1		Enzyme kinetic model of the in vitro cleavage of carotenoids and apocarotenoids by Arabidopsis carotenoid cleavage dioxygenase 4 (CCD4).
Chen_MSB2009	v1		Large mass-action reaction network model of ErbB receptor signaling to ERK and AKT in response to EGF and heregulin.
Crauste_CellSystems2017	v1		Cell population model of CD8 T cell differentiation (naive, early/late effector, memory), fitted to cell counts in mice after vaccinia virus infection.
Elowitz_Nature2000	v1		Gene regulatory network model of the repressilator, a synthetic three-gene transcriptional oscillator in E. coli, fitted to a fluorescence time course.
Fiedler_BMCSystBiol2016	v1		Reaction network model of RAF/MEK/ERK signaling with ERK feedback in HeLa cells, fitted to phospho-MEK/ERK data without and with RAF or MEK inhibitors.
Froehlich_CellSystems2018	v1		Large-scale reaction network model of cancer signaling pathways, parameterized by cell-line-specific gene expression and fitted to drug response viability data.
Fujita_SciSignal2010	v1		Reaction network model of EGF-induced EGFR, Akt and S6 phosphorylation in PC12 cells under different EGF stimulation profiles.
Giordano_Nature2020	v1		Compartmental epidemiological model (SIDARTHE, distinguishing detected/undetected and symptom severity) of the COVID-19 epidemic in Italy.
Isensee_JCB2018	v1		Reaction network model of cAMP-induced PKA activation and phosphorylation of its regulatory subunit in rat sensory neurons under pharmacological perturbations.
Lang_PLOSComputBiol2024	v1		Reaction network model of the human cell cycle, generated from a rule-based model and fitted to compartment-resolved single-cell imaging data of RPE-1 cells.
Laske_PLOSComputBiol2019	v1		Reaction network model of intracellular influenza A virus replication, fitted to viral RNA, genome import and virus release data in human A549 cells.
Liu_IFACPapersOnLine2025	v1		Population model of oncolytic virus therapy with uninfected and infected tumor cells and virus, fitted to tumor size data in zebrafish.
Lucarelli_CellSystems2018	v1		Reaction network model of TGF-beta-induced SMAD complex formation and target gene expression.
Okuonghae_ChaosSolitonsFractals2020	v1		Compartmental epidemiological model of COVID-19 in Lagos, Nigeria, including asymptomatic and detected infections.
Oliveira_NatCommun2021	v1		Compartmental epidemiological model of COVID-19 in Bahia, Brazil (state excluding the capital Salvador), with non-detected cases, hospitalization and deaths.
Perelson_Science1996	v1		Within-host model of HIV-1 viral dynamics, fitted to plasma HIV-1 RNA levels after treatment with the protease inhibitor ritonavir.
Rahman_MBS2016	v1		Compartmental epidemiological model of HIV transmission with CD4+ T cell count-based infection stages and treatment, fitted to HIV prevalence in South Africa.
Raia_CancerResearch2011	v1		Reaction network model of IL-13-induced JAK2/STAT5 signaling in the primary mediastinal B-cell lymphoma cell line MedB-1.
Raimundez_PCB2020	v1		Reaction network model of EGFR and MET signaling to ERK and AKT in two gastric cancer cell lines in response to EGF and the anti-EGFR antibody cetuximab.
SalazarCavazos_MBoC2020	v1		Reaction network model of EGFR dimerization, multisite phosphorylation and binding of the adaptor proteins GRB2 and SHC1, generated from a rule-based model.
Schwen_PONE2014	v1	v2	Reaction network model of insulin binding, internalization and degradation in two subpopulations of isolated hepatocytes with low and high insulin binding.
Smith_BMCSystBiol2013	v1		Large reaction network model of insulin signaling in rodent adipocytes and its regulation by oxidative stress.
Sneyd_PNAS2002	v1		Kinetic model of the type-2 IP3 receptor, fitted to time courses of calcium release from liver microsomes at varying IP3 and calcium concentrations.
Weber_BMC2015	v1		Reaction network model of interactions of protein kinase D, a lipid kinase (PI4KIIIbeta) and a ceramide transfer protein (CERT) at the trans-Golgi network.
Zhao_QuantBiol2020	v1		Compartmental epidemiological model of COVID-19 in Wuhan, Hubei and the rest of China, with quarantined and unquarantined infected individuals.
Zheng_PNAS2012	v1		Reaction network model of the combinatorial methylation of lysines 27 and 36 on histone H3.

License

Any original content in this repository may be used under the terms of the BSD-3-Clause license. Different terms may apply to models and datasets, for which we refer the user to the original publications that are referenced in the respective SBML files.

Installation

See INSTALL.md.

How to Cite

Please cite the benchmark collection by citing:

1. the PEtab paper, which first introduced this collection in a peer-reviewed publication: https://doi.org/10.1371/journal.pcbi.1008646
2. the Zenodo repository, where you can cite specific versions of the files: https://doi.org/10.5281/zenodo.8155057

Example BibTeX (customize the Zenodo year and version to match your version):

@misc{petab_benchmark_collection,
  author       = {The PEtab Benchmark Collection contributors},
  title        = {{The PEtab Benchmark Collection of parameter estimation problems}},
  year         = 2024,
  publisher    = {Zenodo},
  version      = {2024.10.15},
  doi          = {10.5281/zenodo.8155057},
  url          = {https://doi.org/10.5281/zenodo.8155057}
}

@article{petab,
    doi = {10.1371/journal.pcbi.1008646},
    author = {Schmiester, Leonard AND Schälte, Yannik AND Bergmann, Frank T. AND Camba, Tacio AND Dudkin, Erika AND Egert, Janine AND Fröhlich, Fabian AND Fuhrmann, Lara AND Hauber, Adrian L. AND Kemmer, Svenja AND Lakrisenko, Polina AND Loos, Carolin AND Merkt, Simon AND Müller, Wolfgang AND Pathirana, Dilan AND Raimúndez, Elba AND Refisch, Lukas AND Rosenblatt, Marcus AND Stapor, Paul L. AND Städter, Philipp AND Wang, Dantong AND Wieland, Franz-Georg AND Banga, Julio R. AND Timmer, Jens AND Villaverde, Alejandro F. AND Sahle, Sven AND Kreutz, Clemens AND Hasenauer, Jan AND Weindl, Daniel},
    journal = {PLOS Computational Biology},
    publisher = {Public Library of Science},
    title = {PEtab—Interoperable specification of parameter estimation problems in systems biology},
    year = {2021},
    month = {01},
    volume = {17},
    url = {https://doi.org/10.1371/journal.pcbi.1008646},
    pages = {1-10},
    abstract = {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.},
    number = {1},
}

References

The model collection has been used in multiple publications.

If you used the benchmark problems in your work and your publication is missing, please let us know via a new GitHub issue.