Story
PREFER: An Ontology for the PREcision FERmentation Community
Key takeaway
Researchers developed an ontology, a structured way to describe and organize knowledge, to help streamline the growing field of precision fermentation. This could enable more efficient production of biofuels, pharmaceuticals, and sustainable foods.
Quick Explainer
PREFER is an open-source ontology that provides a standardized framework for integrating and analyzing the diverse data streams generated in precision fermentation processes. By modeling the key entities and relationships across inputs, processes, and outputs, PREFER enables interoperability between siloed biofoundry datasets. This common semantic model supports advanced analytics and AI applications, helping to address the challenges of scaling precision fermentation through improved data accessibility and knowledge representation. The ontology's community-driven development approach promotes collaborative growth and adoption across the precision fermentation community.
Deep Dive
Technical Deep Dive: PREFER Ontology for Precision Fermentation
Overview
PREFER is an open-source ontology designed to establish a unified standard for bioprocess data in the precision fermentation community. It provides a comprehensive semantic framework for integrating high-throughput bioprocess data, covering operational, environmental and process parameters across different scales of precision fermentation.
Problem & Context
- Precision fermentation relies on engineered microbes to produce sustainable products, but lacks community standards for data accessibility and interoperability across diverse experimental platforms.
- Biofoundries generate vast datasets through high-throughput bioreactor screening, but siloed data prevents cross-platform integration and analysis.
- Existing fermentation ontologies are insufficient for the technological complexity of precision fermentation processes.
Methodology
- PREFER was developed using the Ontology Development Kit, adhering to OBO Foundry standards.
- It aligns with the Basic Formal Ontology and connects to other community ontologies like ChEBI, GO, and Cell Ontology.
- Core concepts include Process Control Variables, Precision Fermentation Processes, Process Measured Variables, Process Calculated Variables, and Material Entities.
- PREFER models the entire data flow of a precision fermentation process, from inputs like strain and media to outputs like products, byproducts, and associated measured/computed variables.
Results
- PREFER provides a standardized vocabulary and formal semantic structure for precision fermentation data.
- It supports integration of heterogeneous data streams (online measurements, offline sampling, omics data) across different scales and platforms.
- The ontology's logical axioms and inferencing capabilities enable advanced analytics and AI applications.
Interpretation
- PREFER helps address key challenges in scaling precision fermentation by enabling:
- Interoperability and data integration across siloed biofoundry datasets
- Standardized metadata for automated execution and high-fidelity data capture
- Machine-actionable knowledge representation for AI-driven process optimization
- The ontology's community-driven development model promotes collaborative growth and adoption.
Limitations & Uncertainties
- PREFER currently focuses on the precision fermentation process itself; future work could expand to integrate downstream processing and sustainability-related domains.
- Widespread adoption requires biofoundries and bioprocess labs to align their data management practices with the ontology.
What Comes Next
- Link biofoundry data to PREFER and develop semantic knowledge graphs for predictive analytics.
- Implement a federated architecture where PREFER serves as the domain ontology and application ontologies bridge to local data models.
- Promote PREFER adoption through community engagement and collaborative ontology development.
Sources: [1] PREFER: An Ontology for the PREcision FERmentation Community (arXiv preprint)