Integration of gene co-expression network and metabolic network based on flux balance anaysis
This document covers instruction on how to run the integration of gene co-expression network and metabolic model method in MATLAB.
(or you can access in the cobra toolbox : https://opencobra.github.io/cobratoolbox/latest/modules/analysis/ICONGEMs/index.html)
- Matlab (version 2018a or better)
- Cobra Toolbox
- Gurobi solver (version 9.0.1 or better, free academic)
- Gene expression profile Note that the first column of gene expression data should have gene symbols/names used in the GPR association of the genome scale metabolic model. First row of gene expression data should have condition names.
Step 1. Open Matlab program and enter:
>> initCobraToolbox;
at Matlab command line.
Step 2. Read SBML model
>> model = readCbModel('model_name');
Step 3. Read gene expression data
>> [exp txt] = xlsread('gene_expression_profile_file.csv');
Where exp is the numeric value of gene expression data in gene_expression_profile_file.csv file and txt is text data in gene_expression_profile_file.csv. Numeric values in inner spreadsheet rows and columns appear as empty character vectors in txt.
Step 4. Perform analysis
>> [solICONGEMs,boundEf] = ICONGEMs(model, exp, txt, condition, threshold,alpha, numericalFlag);
The algorithm of integration of co-expression network and metabolic model is completed by using function ICONGEMs where the input is model file in step2, exp and txt in step 3 and row vector of condition that are wanted to calculate flux distribution (default is all conditions) and threshold for constructing co-expression network (default value is 0.9). The alpha value is the proportion of biomass (value in range (0,1]).Parameter numericFlag is 1 if using Human Recon (Default = 0).
After the algorithm is finished, solICONGEMs for the predicted metabolic fluxes will be added to the Workspace. Numerical flux values can be examined in more detail by double-clicking solICONGEMs. The boundEf is upperbound of E-flux method. Moreover, the output of this algorithm is reported in result.csv file.
Paklao, T., Suratanee, A. & Plaimas, K. ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology. BMC Bioinformatics 24, 492 (2023). https://doi.org/10.1186/s12859-023-05599-0