Timed batch inputs unlock substantially higher yields for enzymatic cascades.

Area of Science:

• Biochemical Engineering
• Synthetic Biology
• Systems Biology

Background:

• Cell-free enzymatic reaction networks (ERNs) offer a platform for synthesizing valuable compounds.
• Emergent dynamics like product inhibition and cofactor competition in ERNs create kinetic barriers, limiting overall yields.

Purpose of the Study:

• To develop a model-guided strategy for optimizing ERNs through time-dependent component addition in batch reactions.
• To overcome kinetic limitations and enhance product yields in complex enzymatic pathways.

Main Methods:

• Introduced a model-guided optimal design strategy for generating time-dependent 'recipes' for batch reactions.
• Applied the strategy to the pentose phosphate pathway and a branched nucleotide salvage pathway.
• Utilized repeated, timed additions of components throughout the reaction process.

Main Results:

• Optimized inputs for the pentose phosphate pathway increased AMP production by up to 5.7-fold and glucose conversion to ~48%.
• Time-dependent dosing in the salvage pathway increased UTP yield ~21-fold compared to all-at-once dosing.
• Demonstrated the ability to balance competing branches and overcome kinetic barriers.

Conclusions:

• Timed batch inputs provide a generally applicable method for optimizing complex reaction sequences in cell-free systems.
• This approach effectively addresses emergent dynamics and kinetic limitations inherent in ERNs.
• Model-guided, time-dependent dosing represents a significant advancement in maximizing product yields from enzymatic networks.