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Protocol for genome-scale differential flux analysis to interrogate metabolic differences from gene expression data

Year: 2024
Type: article
Abstract: Deciphering the functional differences between diseased and healthy cells requires understanding the alterations in biochemical flux patterns. We present a genome-scale differential flux analysis (GS-DFA) protocol to elucidate these metabolic disparities by integrating condition-specific gene expression data into the human genome-scale metabolic model (humanGEM). In this protocol, we describe the steps to normalize and integrate data into the humanGEM and analyze differential flux across the biochemical network between diseased and healthy cells. For complete details on the use and execution of this protocol, please refer to Nanda et al. 1 • Installation guide for necessary software and libraries for GS-DFA • Instructions for preprocessing and normalizing the gene expression data • Steps for integrating the normalized gene expression data into the humanGEM • Detailed procedures for GS-DFA between the diseased cell and control Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Deciphering the functional differences between diseased and healthy cells requires understanding the alterations in biochemical flux patterns. We present a genome-scale differential flux analysis (GS-DFA) protocol to elucidate these metabolic disparities by integrating condition-specific gene expression data into the human genome-scale metabolic model (humanGEM). In this protocol, we describe the steps to normalize and integrate data into the humanGEM and analyze differential flux across the biochemical network between diseased and healthy cells.
DOI: 10.1016/j.xpro.2024.103291
Language: English
Open Access status: gold
Estimated APC paid: $1,500
OpenAlex ID: W4402232220
FWCI: 0.353
Cites: 19
Cited by: 3
Related to: 10