MiNEApy: enhancing enrichment network analysis in metabolic networks

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Genome-scale metabolic networks (GEMs) model cellular metabolism but enumerating elementary flux modes (EFMs) is computationally challenging.
• Traditional EFM analysis often overlooks crucial metabolic details like co-factor balancing and by-product formation.
• The Minimum Network Enrichment Analysis (MiNEA) method was developed to address these limitations by identifying minimal metabolic networks.

Purpose of the Study:

• To present MiNEApy, a Python package reimplementation of the MiNEA method.
• To enable efficient computation of minimal metabolic networks and facilitate enrichment analysis.
• To demonstrate the utility of MiNEApy for analyzing context-specific metabolic pathways.

Main Methods:

• MiNEApy computes minimal networks based on biomass building blocks and metabolic tasks.
• The package integrates condition-specific omics data (transcriptomics, proteomics, metabolomics) for context-specific analysis.
• MiNEApy was applied to both small-scale and genome-scale models of Escherichia coli.

Main Results:

• MiNEApy successfully computes minimal networks and performs enrichment analysis.
• Demonstrated application on Escherichia coli models highlights the package's capability for minimal network enrichment analysis.
• The study showcases MiNEApy's effectiveness in analyzing context-specific metabolic data.

Conclusions:

• MiNEApy provides a robust and efficient tool for analyzing metabolic pathways.
• The package enhances the understanding of metabolic flexibility and context-specific routes within cells.
• MiNEApy offers a valuable resource for researchers in systems biology and metabolic engineering.