bofdat
Generate biomass objective function stoichiometric coefficients for genome-scale models from experimental data
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Keywords
biomass-objective-function
genome-scale-models
machine-learning
metabolic-flux-analysis
metabolic-models
Last synced: 11 months ago
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Generate biomass objective function stoichiometric coefficients for genome-scale models from experimental data
Basic Info
Statistics
- Stars: 26
- Watchers: 6
- Forks: 6
- Open Issues: 9
- Releases: 3
Topics
biomass-objective-function
genome-scale-models
machine-learning
metabolic-flux-analysis
metabolic-models
Created about 9 years ago
· Last pushed about 3 years ago
Metadata Files
Readme
License
README.rst
|License| |Documentation|
BOFdat
======
Generate biomass objective function for genome-scale models from experimental data.
BOFdat is a three step workflow that allows modellers to generate a complete biomass objective function *de novo* from experimental data:
1. Obtain stoichiometric coefficients for major macromolecules and calculate maintenance cost
2. Find coenzymes and inorganic ions
3. Find metabolic end goals
Significance
------------
Genome-scale metabolic models rely both on a defined media and a precise biomass objective function to generate reliable predictions of flux-states and gene essentiality. Generate a biomass objective that is specific to your organism of interest by incorporating experimental data and calculating stoichiometric coefficients. This package aims to produce an easy way to generate biomass stoichiometric coefficients that reflect experimental reality by incorporating weight fractions and relative abundances of macromolecules obtained from multiple OMICs datasets and finding specie-specific metabolic end goals.
Installation
~~~~~~~~~~~~
Use pip to install BOFdat from `PyPi`_::
pip install BOFdat
.. _PyPi: https://pypi.org/project/BOFdat/
Example use
~~~~~~~~~~~
A full biomass objective function stoichiometric coefficients determination from experimental data fetched from literature for the *E.coli* model *i*ML1515 is available in the Example folder. The files used are also provided.
Documentation
~~~~~~~~~~~~~
The documentation and API for BOFdat is available on `Read the Docs`_
.. _Read the docs: http://BOFdat.readthedocs.org/
Cite
----
|BOFdat Generating biomass objective functions for genome-scale metabolic models from experimental data|_
.. _BOFdat Generating biomass objective functions for genome-scale metabolic models from experimental data: https://doi.org/10.1371/journal.pcbi.1006971
.. |BOFdat Generating biomass objective functions for genome-scale metabolic models from experimental data| replace:: BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data
.. |License| image:: https://img.shields.io/badge/License-MIT-blue.svg
:target: https://github.com/jclachance/BOFdat/blob/master/LICENSE
.. |Documentation| image:: https://readthedocs.org/projects/BOFdat/badge/?version=master
:target: https://bofdat.readthedocs.io/en/latest/index.html
Author: Jean-Christophe Lachance
Date: 06-13-2018
Version: 0.1.4
Owner
- Name: Jean-Christophe Lachance
- Login: jclachance
- Kind: user
- Location: Sherbrooke
- Company: Université de Sherbrooke
- Repositories: 2
- Profile: https://github.com/jclachance
PhD candidate, Bioinformatics, Université de Sherbrooke. My research focuses on the Systems Biology of minimal cells.
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Last Year
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Last synced: almost 3 years ago
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| Name | Commits | |
|---|---|---|
| jclachance | j****e@e****u | 268 |
| EBosi | b****e@g****m | 3 |
| coltonlloyd | c****d@e****u | 3 |
| Moritz E. Beber | m****r@p****t | 1 |
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- Total pull requests: 21
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dependencies (1)
python (1)
Dependencies
BOFdat/core/requirements.txt
pypi
- BOFdat *
- cobra >0.11
- deap *
- matplotlib *
- numpy >=1.13
- pandas *
- pebble *
- scikit-learn ==0.18.1
- scipy ==0.19.1
BOFdat.egg-info-old/requires.txt
pypi
- BioPython *
- cobra *
docs/requirements.txt
pypi
- Sphinx *
- ipykernel *
- nbsphinx >=0.2.4
- sphinx-autoapi *
- sphinxcontrib-napoleon *
requirements.txt
pypi
- cobra >0.11
- deap *
- matplotlib *
- numpy >=1.13
- pandas *
- pebble *
- scikit-learn ==0.18.1
- scipy ==0.19.1
setup.py
pypi
- BioPython *
- cobra >=0.11.0
- deap *
- matplotlib *
- numpy >=1.13
- pebble *
- scikit-learn >=0.18
- scipy *
- seaborn *