A property graph schema for automated metadata capture, reproducibility and knowledge discovery in high-throughput bioprocess development

Abstract

Recent advances in autonomous experimentation and self-driving laboratories have drastically increased the complexity of orchestrating robotic experiments and of recording the different computational processes involved including all related metadata. Addressing this challenge requires a flexible and scalable information storage system that prioritizes the relationships between data and metadata, surpassing the limitations of traditional relational databases. To foster knowledge discovery in high-throughput bioprocess development, the computational control of the experimentation must be fully automated, with the capability to efficiently collect and manage experimental data and their integration into a knowledge base. This work proposes the adoption of graph databases integrated with a semantic structure to enable knowledge transfer between humans and machines. To this end, a property graph schema (PG-schema) has been specifically designed for high-throughput experiments in robotic platforms, focused mainly on the automation of the computational workflow used to ensure the reproducibility, reusability, and credibility of learned bioprocess models. A prototype implementation of the PG-schema and its integration with the workflow management system using simulated experiments is presented to highlight the advantages of the proposed approach in the generation of FAIR data.