PEtab is a data format for specifying parameter estimation problems in systems biology. This repository contains the PEtab specifications and additional documentation.

PEtab is built around SBML and based on tab-separated values (TSV) files. It is meant as a standardized way to provide information for parameter estimation, which is out of the current scope of SBML. This includes, for example:

• Specifying and linking measurements to models

  • Defining model outputs

  • Specifying noise models

• Specifying parameter bounds for optimization

• Specifying multiple simulation conditions with potentially shared parameters

Documentation of the PEtab data format is available at https://petab.readthedocs.io/en/latest/.

To participate in PEtab editor elections, discussions on new PEtab developments, or PEtab-related events, please join our petab-discuss mailing list.

Any contributions and feedback to PEtab are very welcome, see our contribution guide.

A wide range of PEtab examples can be found in the systems biology parameter estimation benchmark problem collection.

Where PEtab is supported (in alphabetical order):

• AMICI (Example)

• A PEtab -> COPASI converter

• d2d (HOWTO)

• dMod (HOWTO)

• MEIGO (HOWTO)

• parPE

• PEtab.jl (HOWTO)

• PumasQSP.jl (HOWTO)

• pyABC (Example)

• pyPESTO (Example)

• SBML2Julia (Tutorial)

If your project or tool is using PEtab, and you would like to have it listed here, please let us know.

The following list provides an overview of supported PEtab features in different tools, based on passed test cases of the PEtab test suite:

Legend:

• First character indicates whether computing simulated data is supported and simulations are correct ( ) or not (-).
• Second character indicates whether computing chi2 values of residuals are supported and correct ( ) or not (-).
• Third character indicates whether computing likelihoods is supported and correct ( ) or not (-).

If you would like to use PEtab yourself, please have a look at:

• a PEtab tutorial going through the individual steps of setting up a parameter estimation problem in PEtab, independently of any specific software
• the PEtab format reference
• the example models provided in the benchmark collection.
• the tutorials provided with each of the softwares supporting PEtab

To convert your existing parameter estimation problem to the PEtab format, you will have to:

1. Specify your model in SBML.

2. Create a condition table.

3. Create a table of observables.

4. Create a table of measurements.

5. Create a parameter table.

6. Create a YAML file that lists the model and all of the tables above.

If you are using Python, some handy functions of the PEtab library can help you with that. This includes also a PEtab validator called petablint which you can use to check if your files adhere to the PEtab standard. If you have further questions regarding PEtab, feel free to post an issue at our GitHub repository.

PEtab comes with a Python package for creating, checking, visualizing and working with PEtab files. This package is available at https://github.com/PEtab-dev/libpetab-python/.

There is also an R package for PEtab in development, which currently supports: consistent manipulation of experimental conditions, measurements, and observables; and visualizing measurements. It also provides an interface to the PEtab Python library, and an interface to dMod that supports: conversion of dMod models to SBML; and simulation and visualization of results. The package is available at https://github.com/dlill/petab/.

If you have any questions or problems with PEtab, feel free to post them at our GitHub issue tracker.