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?
You can follow the [introduction](docs/index.ipynb).
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).
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 Recon3:
Python 3.10.8 (main, Oct 24 2022, 10:07:16) [GCC 12.2.0] Type 'copyright', 'credits' or 'license' for more information IPython 8.4.0 -- An enhanced Interactive Python. Type '?' for help. In : from cobra.io import load_model In : from corda import benchmark In : ecoli = load_model("iJO1366") Restricted license - for non-production use only - expires 2023-10-25 In : opt = benchmark(ecoli) Running setup for model `iJO1366`. Running CORDA setup... ✔ [0.479 s] Running CORDA build... ✔ [7.44 s] Running validation on reduced model... ✔ [0.448 s] In : print(opt) build status: reconstruction complete Inc. reactions: 447/2583 - unclear: 0/0 - exclude: 446/2582 - low and medium: 0/0 - high: 1/1 In : recon3 = load_model("Recon3D") In : opt = benchmark(recon3) Running setup for model `Recon3D`. Running CORDA setup... ✔ [2 s] Running CORDA build... ✔ [13.7 s] Running validation on reduced model... ✔ [1.68 s] In : print(opt) build status: reconstruction complete Inc. reactions: 114/10600 - unclear: 0/0 - exclude: 113/10599 - low and medium: 0/0 - high: 1/1