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.

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

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Article information

Article type
Paper
Submitted
19 Feb 2025
Accepted
10 Jun 2025
First published
12 Jun 2025
This article is Open Access
Creative Commons BY license

Digital Discovery, 2025, Advance Article

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

F. M. Mione, M. F. Luna, L. Kaspersetz, P. Neubauer, E. C. Martinez and M. N. Cruz Bournazou, Digital Discovery, 2025, Advance Article , DOI: 10.1039/D5DD00070J

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