Automation of Experimental Workflows for High Throughput Robotic Cultivations

Abstract

Process systems engineering methods and tools have been difficult to apply in bioprocess engineering, mainly due to the high complexity of biological systems and the low reproducibility of the experiments. High throughput robotic cultivation platforms in combination with computational tools for experimental design, resource scheduling, and operation, are rapidly gaining popularity. One important contribution being the generation of data in high throughput needed to overcome this lack of data with high information content and the worrying reproducibility crisis in life sciences. In this work, directed acyclic graphs are used to represent, manage and track all experimental workflows in a robotic platform. They support data provenance and enable traceability and reproducibility of workflows in robotic facilities. The experimental workflows are automated using Apache Airflow enabling to manage all necessary steps for fed-batch cultivations, including sampling, sample transport by a mobile robot, feed additions, data collection, storage in a SQL database and model fitting. The added value of this system is demonstrated in scale-down experiments, where E. coli BL21 (DE3), producing elastin like proteins, exhibits robustness towards glucose oscillations that mimic industrial cultivation conditions.