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bioRxiv · 10.64898/2026.06.04.730034

CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI

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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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.. 2026-06-07. CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI. https://doi.org/10.64898/2026.06.04.730034

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