pymCADRE 1.2.1

The pymCADRE tool is an advanced re-implementation of the metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE) algorithm in Python. It constructs tissue-specific metabolic models by leveraging gene expression data and literature-based evidence, along with network topology information.

pymCADRE

Authors : Nantia Leonidou

Overview.

The pymCADRE tool is an advanced re-implementation of the metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE) algorithm in Python. It constructs tissue-specific metabolic models by leveraging gene expression data and literature-based evidence, along with network topology information.

The reactions within the generic global model are being ranked, and the ones with the lowest supporting evidence for the tissue of interest are given the highest priority for removal:

GM, C, NC, P, Z, model_C = rank_reactions(model, G, U, confidence_scores, C_H_genes, method)

If the generic functionality test is passed, the model undergoes pruning, which results in a context-specific reconstruction:

PM, cRes = prune_model(GM, P, C, Z, eta, precursorMets, salvage_check, C_H_genes, method)

Prerequisites

This tool has the following dependencies:

python 3.8

Packages:

• pandas
• numpy
• cobra
• requests
• os

Input data

• model: COBRA model structure for the metabolic model of interest
• precursorMets: list of precursor, key, metabolites in form of .txt file
• confidence_scores: literature/experimental-based confidence assigned to reactions in model

Tissue-specific expression evidence:

• G: list of Entrez IDs for all genes in model
• U: list of ubiquity scores calculated for all genes in model

Optional Inputs

• salvageCheck: flag whether to perform a functional check for the nucleotide salvage pathway (1) or not (0)
• C_H_genes: list with Entrez IDs for genes with particularly strong evidence of activity in the tissue of interest
• method: method to use internal optimizations, (1) flux variability analysis or (2) fastcc

Outputs

• PM: pruned COBRA tissue-specific model
• GM: COBRA model after removing blocked reactions from the input global model
• C: core reactions in GM
• NC: non-core reactions in GM
• Z: reactions with zero expression across all samples after binarization
• model_C: core reactions in the generic model (including blocked reactions)
• pruneTime: total reaction pruning time
• cRes: result of model checks (consistency/function) during pruning

Usage

To run pymCADRE, execute the notebook named main_pymcadre.ipynb or the python script named pymcadre.py. The scripts can be modified to the preferred parameters and input files. Jupyter notebooks with test runs and test scripts are also provided as reference points.