SBMLKinetics

SBMLKinetics is a Python package to evaluate and classify kinetics in SBML models. There are many possible kinetics like the zeroth order, mass action, Michaelis-Menten, Hill kinetics and others. This work characterizes the kinetics in the BioModels Database as an example to improve modeling best practices. Our tool can analyze any data sets with SBML files as input. Users can also use this tool to compare different data sets. For instance, we compare the distribution of kinetics for the signaling and metabolic networks and find the substantial differences between two types of networks.

Citing

If you are using any of the code, please cite the article (https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05380-3) and the PYPI web page (https://pypi.org/project/SBMLKinetics/).

For users

Installation

pip install SBMLKinetics

A Classification Example

Here is a classification example generated by SBMLKinetics:

Please see more examples in the documentation.

Documentation

Please see the documentation at https://sunnyxu.github.io/SBMLKinetics/ for details.

For developers

Setup environment

Install spyder3
Clone the SBMLKinetics repository using git clone https://github.com/SunnyXu/SBMLKinetics
Create a virtual environment for the project.
- cd SBMLKinetics
- python -m venv kv
- source kv/Scripts/activate (Use "\" in windows.)
- pip install -r requirements.txt
- deactivate

To verify the setup:

Return to the SBMLKinetics directory.
source kv/Scripts/activate (Use "\" in windows.)
export PYTHONPATH=`pwd`
python tests/test_simple_sbml.py. The tests should run without error. (Use "\" in windows.)

Running Codes

cd SBMLKinetics
source kv/bin/activate (Use "\" in windows.) When you're done, use deactivate.

Documentation

examples/tutorial.py has code illustrating usage
SBMLKinetics/common/*.py has codes for the SmpleSBML (simple_sbml.py), Reaction (reaction.py), and KineticLaw (kinetic_law.py).

Metadata

Release files for SBMLKinetics 1.1.1

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

Source distribution (sdist)

Source distribution for SBMLKinetics 1.1.1
File	Size	Uploaded
SBMLKinetics-1.1.1.tar.gz	43.6 MB	Apr 23, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for SBMLKinetics 1.1.1
File	Interpreter	ABI	Platform	Reset
SBMLKinetics-1.1.1-py3-none-any.whl 87.1 MB Apr 23, 2024	Python 3	none	any	Details

Total release size: 130.7 MB

Release files / SBMLKinetics-1.1.1.tar.gz

Download URL	SBMLKinetics-1.1.1.tar.gz
Size	43.6 MB
Tags	Source
SHA-256 checksum How to use checksums	`2f1608f5e49561f23920f6f06c93b4d2814ccb80c3dcc7109114650b77ee7d2f`
BLAKE2b-256 checksum How to use checksums	`9360ba0109ca2ede261c7a1eee8b0f6bbb83d7c68964052f83c55883c5ecde59`
Upload date	Apr 23, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.28.2 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.3`

Release files / SBMLKinetics-1.1.1-py3-none-any.whl

Download URL	SBMLKinetics-1.1.1-py3-none-any.whl
Size	87.1 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`4aa6db05256e68a802b147063f322495c3c2aac229d98dfd579b6bd4fc52530a`
BLAKE2b-256 checksum How to use checksums	`b4de503104d87e69f077fdd11e6691b8d60975a677e18783a1659def5cc71ee4`
Upload date	Apr 23, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.28.2 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.3`