Identification of functional differences in metabolic networks using comparative genomics and constraint-based models

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Genome-scale metabolic network reconstructions are crucial for understanding cellular metabolism.
• Comparing these models reveals metabolic differences but current reaction-level approaches are time-consuming and don't assess functional impact.
• Identifying functional differences requires a method that links gene content to metabolic capabilities.

Purpose of the Study:

• To develop a novel computational approach, CONGA (Comparison Of Network Gene-level Differences Algorithm), for comparing genome-scale metabolic networks at the gene level.
• To identify functional metabolic differences and unique capabilities arising from variations in gene content between reconstructions.
• To demonstrate CONGA's utility in model development, comparative analysis, and antimicrobial target identification.

Main Methods:

• Developed a bilevel mixed-integer programming approach (CONGA) for gene-level comparison of metabolic network reconstructions.
• Identified orthologous genes across networks and analyzed conditions where gene deletions alter flux, indicating functional differences.
• Applied CONGA to compare Escherichia coli models, aid Synechococcus sp. PCC 7002 model development, and identify potential antimicrobial targets in pathogens.

Main Results:

• CONGA successfully identified functional differences in metabolic capabilities between two Escherichia coli reconstructions, pinpointing reactions responsible for distinct chemical production.
• The method facilitated the development of a genome-scale model for Synechococcus sp. PCC 7002.
• CONGA highlighted potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus by revealing metabolic distinctions.

Conclusions:

• CONGA offers a rapid, gene-centric method for functional comparison of metabolic models, overcoming limitations of reaction-level approaches.
• This approach effectively identifies structural metabolic differences that lead to unique functional capabilities.
• CONGA provides a generalizable framework applicable to diverse biological systems for comparative metabolic analysis.