Metadata-Version: 2.0
Name: geckopy
Version: 0.0.1
Summary: Methods for using the GECKO model with cobrapy
Home-page: https://github.com/Biosustain/geckopy
Author: Bejamin Sanchez
Author-email: bensan@chalmers.se
License: custom..
Description-Content-Type: UNKNOWN
Keywords: geckopy
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Dist: cobra (>=0.8.2)

<img src="https://github.com/SysBioChalmers/GECKO/blob/master/GECKO.png" alt="GECKO logo" width="300">

The **GECKO** toolbox is a Matlab/Python package for enhancing a **G**enome-scale model to account for **E**nzyme **C**onstraints, using **K**inetics and **O**mics. It is the companion software to the following publication:

_Benjamin J. Sanchez, Cheng Zhang, Avlant Nilsson, Petri-Jaan Lahtvee, Eduard J. Kerkhoven, Jens Nielsen (2017). Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. [Molecular Systems Biology, 13(8): 935](http://www.dx.doi.org/10.15252/msb.20167411)_

GECKO was written by Benjamin J. Sanchez (@BenjaSanchez), Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, and Cheng Zhang, Science for Life Laboratory, KTH - Royal Institute of Technology.

The software comes in two flavors, Python and Matlab scripts to fetch online data and build the published ecYeast7 GECKO models, and a Python package which can be used with [cobrapy](https://opencobra.github.io/cobrapy/) to obtain a ecYeast7 model object, optionally adjusted for provided proteomics data.

## Scripts for building a GECKO model

Only tested on Windows, probably works on other platforms as well with adjusted installation procedure.

### Required software for running the Python scripts

* [Python](https://www.python.org/) 2.7
* setuptools for python 2.7, accessible [here](http://www.lfd.uci.edu/~gohlke/pythonlibs/#setuptools)
* SOAPpy: for this, open command prompt as admin, and then do:
```
cd C:\Python27\Scripts
easy_install-2.7 SOAPpy
```

### Required software for using the Matlab scripts

* [MATLAB](http://www.mathworks.com/) (7.5 or higher) + Optimization Toolbox.
* The [COBRA toolbox for MATLAB](https://github.com/opencobra/cobratoolbox). Note that [libSBML](http://sbml.org/Software/libSBML) and the [SBML toolbox](http://sbml.org/Software/SBMLToolbox) should both be installed. Both of them are free of charge for academic users. Aditionally, you should add the cobra folder to your MATLAB search path.

## Usage

See the supporting information of [Sánchez et al. (2017)]([citation](https://dx.doi.org/10.15252/msb.20167411))

## Using the geckopy Python package for obtaining an adjusted GECKO model object

If all you need is the ecYeast7 model to use together with cobrapy you can use the `geckopy` Python package.

### Required software

* Python 2.7, 3.4, 3.5 or 3.6
* cobrapy

### Installation

```
pip install geckopy
```

### Usage

```python
from geckopy import GeckoModel
import pandas
some_measurements = pandas.Series({'P00549': 0.1, 'P31373': 0.1, 'P31382': 0.1})
model = GeckoModel('multi-pool')
model.limit_proteins(some_measurements)
model.optimize()
```


=======
History
=======

0.0.1 (2017-09-07)
------------------

* First release on PyPI.


