CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI
Year: 2026
Type: preprint
Abstract: Enzyme cascades enable complex biochemical transformations, but optimizing them requires searching high-dimensional spaces of enzyme stoichiometry, reaction conditions, 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 assisted by agentic analyses. We demonstrate the platform across two three-enzyme 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 reached comparable optima with threefold fewer experimental conditions than Design of Experiments. A multi-agent AI system automated processing 2.1 GB of experimental data, pattern recognition, and insight synthesis. Operating without human intervention for 7 days, CascadeMAP processed >295,000 reactions across ∼7,400 different conditions. This capability establishes a generalizable, resource-efficient framework for autonomously optimizing enzyme cascades and metabolic pathways and accelerates the development of biocatalytic and synthetic biological systems.
Authors: Michal Vasina, David Kovar, Martin Kizovsky, David Lacko, Pavel Vanacek, Maximilian Herich, Eduard Volf, Lukas Drdla, Sona Cabalova, Pavlina Sikorova, Michael Jirasek, Pavel Solansky, Jan Jezek, Ota Samek, Filip Dousek, Hynek Walner, Pavel Zemanek, Andrew deMello, Zdenek Pilat, Jiri Damborsky, Stavros Stavrakis, Stanislav Mazurenko, Zbynek Prokop
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7163806566