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Area of Science:

  • Biotechnology
  • Artificial Intelligence
  • Data Science

Background:

  • AI and machine learning are revolutionizing biotechnology.
  • Current impact is limited by inadequate data infrastructure, with data quality, standardization, and interoperability as key bottlenecks.

Purpose of the Study:

  • To review the limitations of current data infrastructure in AI-enabled biomanufacturing.
  • To advocate for a data-centric approach to overcome these challenges and advance the field.

Main Methods:

  • Review of current literature and practices in AI and biomanufacturing.
  • Analysis of bottlenecks in the design-build-test-learn cycle.
  • Identification of essential data practices for AI advancement.

Main Results:

  • Data quality, standardization, and interoperability are critical limitations.
  • The manual test-to-learn data ingestion step introduces significant latency.
  • Curated repositories, consistent metadata, real-time validation, and efficient learning strategies are essential.

Conclusions:

  • A data-centric strategy is necessary for AI-enabled biomanufacturing.
  • Addressing data limitations will enable scalable, reliable, and autonomous virtual laboratories.
  • Adherence to Findable, Accessible, Interoperable, Reusable (FAIR) principles is vital.