SSDesign: Computational metabolic pathway design based on flux variability using elementary flux modes

Area of Science:

• Metabolic Engineering
• Computational Biology
• Synthetic Biology

Background:

• Metabolic pathway modification using stoichiometric models enhances microbial bio-production.
• Flux variability analysis identifies optimal pathways for growth-associated and non-growth-associated production.

Purpose of the Study:

• Introduce SSDesign, a novel computational method for visually designing gene knockout strategies.
• Identify gene knockouts to eliminate undesirable elementary flux nodes (EFMs) from the solution space.
• Optimize succinate production in Escherichia coli for both growth-associated and non-growth-associated scenarios.

Main Methods:

• Developed and applied the SSDesign computational method.
• Utilized flux variability analysis to define the solution space.
• Predicted gene knockouts for enhanced succinate production in E. coli.
• Experimentally validated predicted gene deletion mutants.

Main Results:

• SSDesign predicted deletion mutants promoting succinate production at maximum biomass yield for growth-associated production.
• A candidate mutant (ΔptsG ΔpykA,F ΔpflA) was experimentally confirmed as a succinate producer.
• SSDesign successfully predicted gene knockout combinations for high growth yield.
• Identified strong candidates for non-growth-associated succinate production (e.g., ΔpntAB ΔsfcA ΔpykA,F).

Conclusions:

• SSDesign is an effective tool for designing metabolic pathways through gene knockouts.
• The method enables the prediction of strains for both growth-associated and non-growth-associated bio-production.
• Experimental validation confirmed the computational predictions for enhanced succinate production in E. coli.