Cloud-Orchestrated Autonomous Bioreactor Arrays for Closed-Loop Strain Characterization

Abstract

Biomanufacturing of biofuels, food, and energetic materials is increasingly important for defense, security, and disaster response operations, where supply-chain resilience and field-forward production are critical. Here, we present a cloud-orchestrated, edge-executed autonomous biomanufacturing platform built around an open-source, parallel mini-bioreactor array. The system integrates in situ sensing, state and growth-parameter estimation under uncertainty, and closed-loop Bayesian optimization to autonomously plan and execute experiments. We show that the platform identifies the optimal growth temperature of Escherichia coli in a single continuous run completed in under 24 hours, and enables parallel screening of Rhodopseudomonas palustris to identify favorable carbon sources and temperatures. This work demonstrates a deployable sense–reason–act autonomy loop on a real physical system.