corda 0.3.4

Genome-scale model construction with CORDA

CORDA for Python

This is a Python implementation based on the papers of Schultz et. al. with some added optimizations. It is based on the following two publiactions:

• Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

• IDENTIFYING CANCER SPECIFIC METABOLIC SIGNATURES USING CONSTRAINT-BASED MODELS

This Python package is developed in the Human Systems Biology Group of Prof. Osbaldo Resendis Antonio at the National Institute of Genomic Medicine Mexico and includes recent updates to the method (CORDA 2).

How to cite?

This particular implementation of CORDA has not been published so far. In the meantime you should if you cite the respective publications for the method mentioned above and provide a link to this GitHub repository.

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 to work. After having a working Python installation with pip (Anaconda or Miniconda works fine here as well) you can install corda with pip

pip install corda

This will download and install cobrapy as well. I recommend using a version of pip that supports manylinux builds for faster installation (pip>=8.1).

For now the master branch is usually working and tested whereas all new features are kept in its own branch. To install from the master branch directly use

pip install https://github.com/resendislab/corda/archive/master.zip

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://resendislab.github.io/corda.

What’s the advantage over other reconstruction algorithms?

No commercial solver needed

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 glpk version is a bout 3 times faster than the fastest tested commercial solver (cplex).

Fast reconstructions

CORDA for Python uses a strategy similar to FastFVA, where a previous solution basis is recycled repeatedly.

Some reference times for reconstructing the minimal growing models for iJO1366 (E. coli) and Recon 2.2:

In [1]: from cobra.test import create_test_model

In [2]: from cobra.io import read_sbml_model

In [3]: from corda import CORDA

In [4]: ecoli = create_test_model("ecoli")

In [5]: conf = {}

In [6]: for r in ecoli.reactions:
   ...:     conf[r.id] = -1
   ...:

In [7]: conf["Ec_biomass_iJO1366_core_53p95M"] = 3

In [8]: %time opt = CORDA(ecoli, conf)
CPU times: user 425 ms, sys: 41.5 ms, total: 466 ms
Wall time: 466 ms

In [9]: %time opt.build()
CPU times: user 13.6 s, sys: 734 µs, total: 13.6 s
Wall time: 13.6 s

In [10]:

In [10]: recon2 = read_sbml_model("/home/cdiener/Downloads/recon2.xml")
cobra/io/sbml.py:235 UserWarning: M_h_x appears as a reactant and product FAOXC220200x

In [11]: conf = {}

In [12]: for r in recon2.reactions:
    ...:     conf[r.id] = -1
    ...:

In [13]: conf["biomass_reaction"] = 3

In [14]: %time opt = CORDA(recon2, conf)
CPU times: user 1.39 s, sys: 149 ms, total: 1.54 s
Wall time: 1.55 s

In [15]: %time opt.build()
CPU times: user 54.2 s, sys: 0 ns, total: 54.2 s
Wall time: 54.3 s