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PEtab – a data format for specifying parameter estimation problems in systems biology

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PEtab is a data format for specifying parameter estimation problems in systems biology. This repository provides extensive documentation and a Python library for easy access and validation of PEtab files.

About PEtab

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 condition with potentially shared parameters
PEtab files

Documentation

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

Examples

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

PEtab support in systems biology tools

Where PEtab is supported (in alphabetical order):

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

PEtab features supported in different tools

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

ID Test AMICI
>=0.10.20		 Copasi D2D dMod MEIGO parPE
develop		 pyABC
>=0.10.1		 pyPESTO
>=0.0.11
1 Basic simulation +++ +– +++ +++ +++ –+ +++ +++
2 Multiple simulation conditions +++ +– +++ +++ +++ –+ +++ +++
3 Numeric observable parameter overrides in measurement table +++ +– +++ +++ +++ –+ +++ +++
4 Parametric observable parameter overrides in measurement table +++ +– +++ +++ +++ –+ +++ +++
5 Parametric overrides in condition table +++ +– +++ +++ +++ –+ +++ +++
6 Time-point specific overrides in the measurement table — — +++ +++ +++ — — —
7 Observable transformations to log10 scale +-+ +– +++ ++- +++ –+ +-+ +-+
8 Replicate measurements +++ +– +++ +++ +++ –+ +++ +++
9 Pre-equilibration +++ +– +++ +++ +++ –+ +++ +++
10 Partial pre-equilibration +++ — +++ +++ +++ –+ +++ +++
11 Numeric initial concentration in condition table +++ +– +++ +++ +++ –+ +++ +++
12 Numeric initial compartment sizes in condition table — +– +++ +++ +++ — — —
13 Parametric initial concentrations in condition table +++ +– +++ +++ +++ –+ +++ +++
14 Numeric noise parameter overrides in measurement table +++ +– +++ +++ +++ –+ +++ +++
15 Parametric noise parameter overrides in measurement table +++ +– +++ +++ +++ –+ +++ +++
16 Observable transformations to log scale +-+ +– +++ ++- +++ –+ +-+ +-+

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 (-).

Using PEtab

If you would like to use PEtab yourself, please have a look at doc/documentation_data_format.rst or at the example models provided in the benchmark collection.

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.

If you are using Python, some handy functions of the PEtab library can help you with that. This include 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 Python library

PEtab comes with a Python package for creating, checking, visualizing and working with PEtab files. This library is available on pypi and the easiest way to install it is running

It will require Python>=3.6 to run.

Development versions of the PEtab library can be installed using

(replace develop by the branch or commit you would like to install).

When setting up a new parameter estimation problem, the most useful tools will be:

  • The PEtab validator, which is now automatically installed using Python entrypoints to be available as a shell command from anywhere called petablint
  • petab.create_parameter_df to create the parameter table, once you have set up the model, condition table, observable table and measurement table
  • petab.create_combine_archive to create a COMBINE Archive from PEtab files

Library examples

Examples for PEtab Python library usage:

Getting help

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

Indices and tables