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Protocol for genome-scale differential flux analysis to interrogate metabolic differences from gene expression data.
Satyajit Beura1, Amit Kumar Das1, Amit Ghosh2
1Department of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
STAR Protocols
|September 5, 2024
Summary
We developed a genome-scale differential flux analysis (GS-DFA) protocol to identify metabolic differences between diseased and healthy cells. This method integrates gene expression data into a human metabolic model for detailed analysis.
Area of Science:
- Biochemistry
- Systems Biology
- Metabolic Engineering
Background:
- Understanding cellular metabolic reprogramming is crucial for distinguishing diseased from healthy states.
- Altered biochemical flux patterns underlie many cellular dysfunctions and diseases.
Purpose of the Study:
- To present a comprehensive protocol for genome-scale differential flux analysis (GS-DFA).
- To elucidate metabolic disparities between diseased and healthy cells by integrating gene expression data.
Main Methods:
- The protocol involves normalizing and integrating condition-specific gene expression data.
- It utilizes the human genome-scale metabolic model (humanGEM).
- Differential flux analysis is performed across the biochemical network.
Main Results:
- The GS-DFA protocol enables the identification of specific metabolic alterations.
- It provides a quantitative comparison of flux patterns between different cellular conditions.
- The method highlights key metabolic pathways affected in disease states.
Conclusions:
- GS-DFA is a powerful approach for dissecting metabolic differences between cell types.
- This protocol facilitates a deeper understanding of disease mechanisms at the metabolic level.
- The findings can inform the development of targeted therapeutic strategies.
