Exploring the combinatorial space of complete pathways to chemicals

Area of Science:

• Metabolic Engineering
• Synthetic Biology
• Computational Biology

Background:

• Designing metabolic pathways computationally presents challenges in balancing co-metabolite and cofactor stoichiometry.
• Integrating reaction rule utilization within a single workflow is complex for existing pathway design tools.

Purpose of the Study:

• To present two complementary, stoichiometry-based computational tools, optStoic and novoStoic, for metabolic pathway design.
• To address challenges in co-metabolite balancing and reaction rule utilization in pathway elucidation.
• To demonstrate the design of novel synthetic routes for isobutanol production.

Main Methods:

• optStoic determines optimal overall conversion stoichiometry, prioritizing performance criteria like carbon/energy efficiency.
• novoStoic expands pathway design to include known and hypothetical reactions using reaction rules derived from a mixed-integer linear programming (MILP) compatible operator.
• Both tools employ stoichiometry-based approaches for comprehensive co-metabolite and cofactor searching.

Main Results:

• optStoic efficiently identifies optimal stoichiometry and minimum reaction steps for desired conversions.
• novoStoic leverages reaction rules to explore natural, engineered, and de novo enzymes, expanding biotransformation possibilities.
• Novel synthetic routes for isobutanol were successfully designed using these computational tools.

Conclusions:

• The developed tools, optStoic and novoStoic, offer robust solutions for complex metabolic pathway design.
• These computational approaches facilitate the exploration of a broader range of enzymatic reactions for biotransformation.
• The successful design of isobutanol synthetic routes highlights the practical applicability of these pathway design strategies.