Computational tools for nonnatural pathway design: Algorithms, applications, and challenges.

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Green Chemistry

Background:

• Biotransformation is crucial for sustainable chemical synthesis.
• Natural pathways are insufficient for many high-value compounds, necessitating nonnatural pathways.
• Nonnatural pathways face challenges like metabolic burden and toxic intermediates.

Purpose of the Study:

• To review computational methods for designing nonnatural metabolic pathways.
• To evaluate the practical application and effectiveness of these methods.
• To bridge the gap between computational predictions and experimental feasibility in synthetic biology.

Main Methods:

• Review of template-based and template-free computational pathway design methods.
• Compilation and analysis of 55 experimentally validated nonnatural pathways.
• Simulation of pathways to identify discrepancies between predictions and empirical results.

Main Results:

• Identified strengths and limitations of current computational pathway design tools.
• Highlighted significant gaps between computational predictions and experimental outcomes.
• Demonstrated the need for integrating computational tools with experimental synthetic biology.

Conclusions:

• Computational methods are valuable for nonnatural pathway design but require refinement.
• Bridging the gap between prediction and practice is essential for advancing microbial cell factories.
• Integrated strategies are proposed to enhance the efficiency and scope of biotransformation.