CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI
Vasina, M.; Kovar, D.; Kizovsky, M.; Lacko, D.; Vanacek, P.; Herich, M.; Volf, E.; Drdla, L.; Cabalova, S.; Sikorova, P.; Jirasek, M.; Solansky, P.; Jezek, J.; Samek, O.; Dousek, F.; Walner, H.; Zemanek, P.; deMello, A.; Pilat, Z.; Damborsky, J.; Stavrakis, S.; Mazurenko, S.; Prokop, Z.
Show abstract
Enzyme cascades enable complex biochemical transformations, but their optimization is resource-intensive, requiring navigation through high-dimensional parameter spaces encompassing reaction conditions, enzyme ratios, and buffer composition. Here we introduce CascadeMAP, an autonomous microfluidic platform for closed-loop optimization of enzyme cascades, integrating high-throughput microfluidics with Bayesian optimization and multi-agent AI system. We demonstrate the platform across two cascades: (i) a glycerol detection pathway monitored by fluorescence and (ii) a 1,2,3-trichloropropane degradation pathway monitored by label-free Raman spectroscopy providing orthogonal detection modalities. Bayesian optimization identified optimal conditions three times faster than Design of Experiments. Multi-agent AI system automated hypothesis generation, processing 11 GB of experimental data, pattern recognition, and insight synthesis. Operating without human intervention for 7 days, CascadeMAP processed [~]220,000 reactions across [~]7,400 different conditions. This capability establishes a generalizable framework for the autonomous optimization of enzyme cascades and metabolic pathways and accelerates the development of biocatalytic and synthetic biological systems.
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