Gene Deletion Algorithms for Minimum Reaction Network Design by Mixed-Integer Linear Programming for Metabolite...

Area of Science:

• Metabolic Engineering
• Systems Biology
• Computational Biology

Background:

• Genome-scale metabolic networks are crucial for simulating growth-coupled production.
• Minimal reaction networks are effective but often conflict with gene-protein-reaction (GPR) relations during gene deletion.
• This conflict hinders the practical realization of designed metabolic pathways.

Purpose of the Study:

• To develop a method for determining gene deletion strategies that enable growth-coupled production.
• To identify minimal sets of genes essential for producing target metabolites while respecting GPR rules.
• To resolve conflicts between reaction network design and gene deletion feasibility.

Main Methods:

• Developed gDel_minRN, a computational tool utilizing mixed-integer linear programming.
• Implemented gDel_minRN to determine gene deletion strategies by maximizing reaction repression under GPR constraints.
• Validated the approach through computational experiments on various metabolic networks.

Main Results:

• gDel_minRN successfully identified core gene sets (30-55% of total genes) for stoichiometrically feasible growth-coupled production.
• The method achieved growth-coupled production for essential vitamins like biotin, riboflavin, and pantothenate.
• The identified core parts are essential for growth-coupled production and are free from GPR conflicts.

Conclusions:

• gDel_minRN provides a robust method for designing minimal metabolic networks for growth-coupled production.
• The tool facilitates biological analysis by pinpointing essential gene-associated reactions.
• This approach aids in the efficient engineering of microbial cell factories for valuable metabolite synthesis.