corda 0.0.5

Genome-scale model construction with CORDA

[](https://travis-ci.org/cdiener/corda) [](https://codecov.io/github/cdiener/corda?branch=master) [](https://landscape.io/github/cdiener/corda/master)

# CORDA for Python

This is a Python implementation based on the paper of Schultz et. al.

[Reconstruction of Tissue-Specific Metabolic Networks Using CORDA](http://journals.plos.org/ploscompbiol/article/authors?id=10.1371%2Fjournal.pcbi.1004808)

## What does it do?

CORDA, short for Cost Optimization Reaction Dependency Assessment is a method for the reconstruction of metabolic networks from a given reference model (a database of all known reactions) and a confidence mapping for reactions. It allows you to reconstruct metabolic models for tissues, patients or specific experimental conditions from a set of transcription or proteome measurements.

## How do I install it

CORDA for Python works only for Python 3.4+ and requires [cobrapy](http://github.com/opencobra/cobrapy) to work. We recommend installation via the [anaconda or miniconda](https://www.continuum.io/downloads) distribution. After installing anaconda or miniconda you can install cobrapy from the bioconda repository

`bash conda install -c bioconda cobra ` After that you can install CORDA using the pip from conda

`bash pip install https://github.com/cdiener/corda/archive/master.zip `

After CORDA for Python comes out of its infancy I will prepare a conda package as well. For now the master branch is usually working and tested whereas all new stuff is kept in the devel branch.

## What do I need to run it?

CORDA requires a base model including all reactions that could possibly included such as Recon 1/2 or HMR. You will also need gene expression or proteome data for our tissue/patient/experimental setting. This data has to be translated into 5 distinct classes: unknown (0), not expressed/present (-1), low confidence (1), medium confidence (2) and high confidence (3). CORDA will then ensure to include as many high confidence reactions as possible while minimizing the inclusion of absent (-1) reactions while maintaining a set of metabolic requirements.

## How do I use it?

A small tutorial is found at https://cdiener.com/corda.

## What’s the advantage over other reconstruction algorithms

I would say there are two major advantages:

1. It does not require any commercial solvers, in fact it works fastest with the free glpk solver that already comes together with cobrapy. For instance for the small central metabolism model (101 irreversible reactions) included together with CORDA the reconstruction uses the following times:

   • cglpk: 0.02 s

   • cplex: 0.30 s

   • gurobi: 0.12 s

   • mosek: 0.23 s

2. It’s fast. CORDA for Python uses a strategy similar to FastFVA, where a previous solution basis is recycled repeatedly (speed-up of ~4-10 times). A normal reconstruction for Recon 1 with mCADRE can take several hours. With the original Matlab implementation of CORDA this takes about 40 minutes and with CORDA for Python it takes less than 5 minutes. A Recon 2 reconstruction can be achieved in less than 30 minutes.