AFP: Finding pathways accounting for stoichiometry along with atom group tracking in metabolic network

Area of Science:

• Metabolic Engineering
• Synthetic Biology
• Computational Biology

Background:

• Automated metabolic pathway discovery is crucial for synthetic biology and metabolic engineering.
• Existing methods often lack detailed atom tracking within stoichiometric models.

Purpose of the Study:

• To introduce a novel method (AFP) for metabolic pathway discovery.
• To integrate atom group tracking into stoichiometric modeling using Mixed Integer Linear Programming (MILP).
• To enable pathway searches from arbitrary starting compounds.

Main Methods:

• Developed the AFP method incorporating atom group tracking into reaction stoichiometry.
• Constructed a Mixed Integer Linear Programming (MILP) model for pathway searching.
• Adapted the MILP model for flexible pathway searches from any compound to a target.

Main Results:

• The AFP method effectively recovers known metabolic pathways.
• AFP demonstrates superior performance compared to existing pathway-finding methods.
• The method successfully discovers biochemically feasible pathways for target metabolites.

Conclusions:

• Combining atom group tracking with stoichiometric MILP modeling enhances metabolic pathway search.
• AFP offers a powerful tool for designing novel and alternative metabolic pathways.
• This approach advances stoichiometric modeling for metabolic engineering applications.