Closed-loop optimization of high-throughput robotic platforms for reproducible bioprocess development

MIONE, FEDERICO; AIZPURU, JUDIT; LUNA, MARTIN; RODRIGUEZ BAHAMON, PABLO; KIM, JONG WOO; MARTINEZ, ERNESTO C.; CRUZ BOURNAZOU, MARIANO N.

Computer aided chemical engineering

Elsevier

Lugar: Amsterdam; Año: 2023; p. 2613 - 2618

To confidently accelerate bioprocess development using high-throughput robotic platforms, parallel experiments should be reproducible whereas the generated data must be FAIR. To this aim, higher levels of automation for planning, redesigning, executing, and monitoring of related experimental-computational workflows are needed. In this work, an automation hierarchy for closed-loop optimization based on Bayesian update, belief propagation and Thompson sampling is proposed. For online redesign of parallel dynamic experiments, asynchronous rolling out beliefs about beliefs based on simulated data is used for counterfactual assessment of the effect of alternative (re)design decisions on the distributions of model parameters. A computational implementation of the Bayesian automation hierarchy using Apache Airflow is described. Simulation results obtained using an E. coli case study are presented to argue that parallel dynamic experiments made using the proposed Bayesian hierarchy are reproducible whereas experimental-computational workflows and data are FAIR by design.