Adding ingredients to a chemical reaction at precise intervals like a choreographed dance increases the final yield by 500 percent.
Complex enzymatic reactions often stall because natural biological signals tell the process to slow down or stop. Chemical production typically involves dumping all the ingredients into a vat at once. Active learning algorithms now design a schedule of timed batch inputs to bypass these natural inhibitions. This precise timing prevents the buildup of waste and keeps the enzymes working at peak performance. Manufacturers can use this method to produce medicines and biofuels much more efficiently than traditional batch processing.
Timed batch inputs unlock significantly higher yields for enzymatic cascades
research_square · rs-5917349
Abstract The dynamic properties of enzymatic reaction networks (ERNs) are difficult to predict due to the emergence of allosteric interactions, product inhibitions and the competition for resources, that all only materialize once the networks have been assembled. In batch systems, the optimization of starting concentrations is challenging, as the composition the reaction mixture changes continuously, prohibiting optimal conditions for the full duration of the reaction. Allowing reagents to be ad