MEMO: A Method for Computing Metabolic Modules for Cell-Free Production Systems.

Area of Science:

• Synthetic biology
• Metabolic engineering
• Computational biology

Background:

• Cell-free bioproduction offers an alternative to microbial fermentation for synthesizing valuable products.
• Identifying efficient metabolic modules is crucial for designing robust cell-free systems.
• Cofactor regeneration (e.g., ATP, NAD(P)H)) is essential for sustained cell-free operation.

Purpose of the Study:

• To present MEMO, a novel computational approach for identifying minimal metabolic modules with specified constraints.
• To enable the design of cell-free systems, particularly focusing on cofactor regeneration.
• To demonstrate MEMO's capability in finding integrated product synthesis and regeneration modules.

Main Methods:

• MEMO utilizes a mixed-integer linear programming approach.
• It incorporates stoichiometric and thermodynamic constraints to find smallest metabolic modules.
• The method searches within large reaction databases like MetaCyc and BiGG.

Main Results:

• MEMO successfully calculated regeneration modules for the synthetic CETCH cycle, meeting complex constraints.
• The most efficient modules identified used glycerol as a substrate and required only 8 enzymatic steps.
• Found modules demonstrated robustness against spontaneous cofactor loss.
• MEMO also identified cell-free systems with integrated product synthesis and cofactor regeneration.

Conclusions:

• MEMO is a powerful computational tool for discovering metabolic modules for cell-free systems.
• It facilitates the design of efficient and robust cofactor regeneration pathways.
• This approach advances the development of cell-free bioproduction technologies.