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Comparative Metabolic Network Flux Analysis to Identify Differences in Cellular Metabolism
Sarah McGarrity1,2, Sigurður T Karvelsson2, Ólafur E Sigurjónsson1,2
1School of Science and Engineering, Reykjavik University, Reykjavik, Iceland.
Methods in Molecular Biology (Clifton, N.J.)
|January 2, 2020
Summary
This study details using the COBRA toolbox to analyze metabolic networks with multi-omics data. It shows how to build and compare models to find metabolic biomarkers and understand cellular changes.
Area of Science:
- Systems biology
- Metabolic network analysis
- Computational biology
Background:
- Genome-scale metabolic reconstructions are crucial for understanding genotype-phenotype links.
- Integrating multi-omics data (transcriptomics, proteomics, metabolomics) provides comprehensive insights.
- The COBRA toolbox facilitates constraint-based analysis for biologists using MATLAB.
Purpose of the Study:
- To outline steps for validating and utilizing published metabolic reconstructions.
- To demonstrate integrating mRNA and metabolomics data for metabolic phenotype modeling.
- To identify metabolic biomarkers and cellular metabolism changes through model comparison.
Main Methods:
- Utilizing the COBRA toolbox in MATLAB for constraint-based metabolic modeling.
- Interrogating the consistency and biological feasibility of metabolic reconstructions.
- Constraining metabolic models with mRNA expression and metabolomics data.
- Comparing derived models to identify metabolic alterations and biomarkers.
Main Results:
- Established a workflow for assessing and applying genome-scale metabolic models.
- Successfully generated context-specific metabolic models using multi-omics data.
- Demonstrated the identification of metabolic biomarkers and changes in cellular metabolism.
Conclusions:
- The COBRA toolbox enables biologists to perform advanced metabolic network analysis.
- Integrating multi-omics data with metabolic reconstructions is key to understanding metabolic phenotypes.
- This approach facilitates the discovery of metabolic biomarkers and insights into cellular metabolism.
