bofdat

Generate biomass objective function stoichiometric coefficients for genome-scale models from experimental data

https://github.com/jclachance/bofdat

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Keywords

biomass-objective-function genome-scale-models machine-learning metabolic-flux-analysis metabolic-models
Last synced: 11 months ago · JSON representation

Repository

Generate biomass objective function stoichiometric coefficients for genome-scale models from experimental data

Basic Info
  • Host: GitHub
  • Owner: jclachance
  • License: other
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 15.5 MB
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  • 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

PhD candidate, Bioinformatics, Université de Sherbrooke. My research focuses on the Systems Biology of minimal cells.

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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 *