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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`	SBML2Julia
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:

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:

Specify your model in SBML.
Create a condition table.
Create a table of observables.
Create a table of measurements.
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

pip3 install petab

It will require Python>=3.6 to run.

Development versions of the PEtab library can be installed using

pip3 install https://github.com/PEtab-dev/PEtab/archive/develop.zip

(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.

Contributing to PEtab

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

Metadata

Release files for petab 0.1.14

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for petab 0.1.14
File	Size	Uploaded
petab-0.1.14.tar.gz	58.6 kB	Jan 12, 2021	Details

Built distribution (wheel)

Table of built distributions (wheels) for petab 0.1.14
File	Interpreter	ABI	Platform	Reset
petab-0.1.14-py3-none-any.whl 70.0 kB Jan 12, 2021	Python 3	none	any	Details

Total release size: 128.7 kB

Release files / petab-0.1.14.tar.gz

Download URL	petab-0.1.14.tar.gz
Size	58.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`4a2b7742e06d494e864055907dc4aedb622dc9d764bd00488fdce08efb324a04`
BLAKE2b-256 checksum How to use checksums	`72504fa4b8544404a2782a02dfb68c9a19b859cf8516b1b86eddb07d6f0102bf`
Upload date	Jan 12, 2021
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.6.12`

Release files / petab-0.1.14-py3-none-any.whl

Download URL	petab-0.1.14-py3-none-any.whl
Size	70.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`1b6d8b5791186bda2a75a9fe8d3fa5b3b0a32e393a254988eda41ffe1157d3df`
BLAKE2b-256 checksum How to use checksums	`bb178122b11e0338463d07ff9bc258f664bfbb10df41f9390992b207ef6db004`
Upload date	Jan 12, 2021
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.6.12`