NetFlow: A tool for isolating carbon flows in genome-scale metabolic networks

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Genome-scale stoichiometric models (GSMs) are crucial for understanding cellular metabolism and predicting metabolic flux.
• Interpreting the complex flux predictions from large GSMs remains a significant challenge.
• Existing methods for pathway reduction lack carbon transition mapping.

Purpose of the Study:

• To develop a novel algorithm, NetFlow, for extracting and quantitatively distinguishing biologically relevant metabolic pathways from GSM flux predictions.
• To address the need for a method that overlays carbon atom transitions onto stoichiometry and flux predictions.
• To enable a deeper mechanistic understanding of metabolic phenotypes.

Main Methods:

• Developed NetFlow, an algorithm leveraging genome-scale carbon mapping and context-specific flux predictions.
• Simulated 13C isotope labeling experiments to calculate carbon yield between metabolites.
• Applied NetFlow to central carbon metabolism in E. coli and a lycopene-producing E. coli GSM.

Main Results:

• NetFlow quantitatively distinguishes carbon flow pathways within GSMs.
• Successfully isolated succinate production pathways and identified mechanisms for increased yield in E. coli knockouts.
• Rapidly identified mechanisms for increased lycopene production following gene knockouts in E. coli.

Conclusions:

• NetFlow provides an interpretable method for analyzing complex metabolic flux predictions.
• The algorithm facilitates in-depth mechanistic understanding of metabolic phenotypes.
• NetFlow is applicable to various GSMs, including those for metabolic engineering applications.