Title: PREFER: An Ontology for the PREcision FERmentation Community
ArXiv ID: 2602.16755
Date: 2026-02-18
Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (원문에 저자 명단이 포함되지 않음) — **
📝 Abstract
Precision fermentation relies on microbial cell factories to produce sustainable food, pharmaceuticals, chemicals, and biofuels. Specialized laboratories such as biofoundries are advancing these processes using high-throughput bioreactor platforms, which generate vast datasets. However, the lack of community standards limits data accessibility and interoperability, preventing integration across platforms. In order to address this, we introduce PREFER, an open-source ontology designed to establish a unified standard for bioprocess data. Built in alignment with the widely adopted Basic Formal Ontology (BFO) and connecting with several other community ontologies, PREFER ensures consistency and cross-domain compatibility and covers the whole precision fermentation process. Integrating PREFER into high-throughput bioprocess development workflows enables structured metadata that supports automated cross-platform execution and high-fidelity data capture. Furthermore, PREFER's standardization has the potential to bridge disparate data silos, generating machine-actionable datasets critical for training predictive, robust machine learning models in synthetic biology. This work provides the foundation for scalable, interoperable bioprocess systems and supports the transition toward more data-driven bioproduction.
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Precision fermentation is a core technology in the emerging bio-based economy, enabling the sustainable production of chemicals and materials through microorganisms engineered for fermentation processes 1 . Applications of precision fermentation now include energy (e.g. biofuels from companies such as LanzaTech) 2 , food (e.g. plant-based meat substitutes from companies such as Impossible Foods) 3 , agriculture (e.g. biofertilizers produced by companies such as Pivot Bio) 4 , and many more. Despite these advances, significant challenges remain in scaling processes from laboratory to industrial systems 5 . This is mainly due to the lack of accessibility of bioprocess data and metadata, collected before, during and after fermentation, which are critical for identifying scale, strain and condition-dependent parameters. Bridging this gap in bioprocess development and scalability is essential to reduce time and cost in biomanufacturing 2 .
Biofoundries, positioned at the interface between academia and industry, play a critical role in addressing the challenges in bioprocess scale-up 6 . In these research institutions, the implementation of high-throughput bioreactor systems/platforms allows parallel experiments, enabling rapid testing and generation of large amounts of data. This screening approach allows researchers to evaluate synthetic engineered biocatalysts at a smaller scale, identifying high performing cell factories for target products. Only the best-performing strains progress to a larger-scale testing. This tiered screening strategy substantially reduces the experimental burden and resource requirements associated with exploring larger bioreactor volumes. All the high-dimensional data generated within these biofoundry infrastructures represent a highvalue asset beyond the scope of any individual experiment. Currently, much of this value remains locked in siloed datasets. To fully benefit, disparate experimental streams would need to be harmonized into a unified analytical framework. However, integrating data across diverse bioreactor platforms, operational modes, measurement techniques and calculation types remains a critical challenge 6,7 . Proprietary software and heterogeneous data outputs provided by different platforms further complicate standardizing resources. This lack of standardized data practices within and across institutions has limited the development of tools for analyzing and visualizing bioprocess data, the ability to exploit multi-omics data, and the application of Artificial Intelligence (AI) to optimize precision fermentation processes effectively 8 . Advancing bio-based production therefore depends heavily on improved bioprocess data management and, above all, data interoperability.
To this end, the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) 9 have become essential guidelines for data-driven biomanufacturing 2 . Initiatives such as the Global Biofoundry Alliance (GBA) 10 , online software tools like EDD (https://edddocs.jbei.org/)
, along with infrastructures like The National Institute of Standards and Technology (NIST Biofoundry) (https://www.nist.gov
) and the Industrial Biotechnology Innovation and Synthetic Biology Accelerator (IBISBA), are actively promoting data standardization, open protocols, and collaborative practices. Despite these efforts, widely adopted open-source frameworks and community-driven platforms that address computational and data-management challenges for precision fermentation are scarce. This lack of accessible tools limits the broader adoption of FAIR principles across academic and industrial settings.
Ontologies, and their related data management systems, are a powerful approach for realizing the FAIR principles 11 ; they improve findability by establishing a semantic framework that supports complex querying, and they provide controlled vocabularies that enhance data interoperability and promote clear data understanding, which in turn facilitates data reusability 12 . Furthermore, data aligned to ontologies can be directly used in AI applications through automated reasoning and integration into machine learning pipelines 13 .
While there are a few existing legacy ontologies in the fermentation domain, their scope is limited to traditional fermentation 14 and fermented food applications 15 . Consequently, these ontologies are insufficient and fail to capture the technological specificity and complexity inherent to precision fermentation processes. To bridge this gap and provide the community with necessary standardisation capabilities, we introduce the PREcision FERmentation (PREFER) ontology. PREFER is a comprehensive semantic framework designed to integrate high-throughput bioprocess data, covering operational, environmental and process parameters across different scales of a precision fermentation process, to accelerate the development and scaling of biosustainable production processes.