FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets.

Area of Science:

• Metabolic Engineering
• Synthetic Biology
• Computational Biology

Background:

• Metabolic engineering is advancing with synthetic biology tools and machine learning (ML).
• Selecting effective genetic targets for metabolic engineering remains a challenge.
• Current ML approaches often require prior biological knowledge, limiting independent use.

Purpose of the Study:

• To present FluxRETAP, a method for identifying genetic engineering targets.
• To leverage genome-scale models (GSMs) and mechanistic knowledge for target prediction.
• To enhance metabolite production through suggested gene overexpression, downregulation, or deletion.

Main Methods:

• Developed FluxRETAP, a computationally inexpensive method.
• Utilized genome-scale models (GSMs) to embed prior mechanistic knowledge.
• Generated prioritized lists of genetic and reaction targets for metabolic engineering.

Main Results:

• FluxRETAP successfully identified 100% of experimentally verified targets for isoprenol production in E. coli.
• The method captured 50% of targets improving taxadiene production in E. coli.
• FluxRETAP identified approximately 60% of targets from a minimal cut-set in Pseudomonas putida, offering additional high-priority candidates.

Conclusions:

• FluxRETAP is an efficient algorithm for suggesting testable genetic and reaction targets in metabolic engineering.
• The method integrates mechanistic knowledge from GSMs to guide strain development.
• FluxRETAP can complement existing ML pipelines in synthetic biology.