Methods for using the GECKO model with cobrapy
Project description
<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.
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.
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