Applying the design-build-test-learn framework to precision fermentation for sustainable food ingredients

Area of Science:

• Synthetic biology
• Food science
• Biotechnology

Background:

• Precision fermentation engineers microorganisms as cell factories for sustainable synthesis.
• Food-grade precision fermentation is constrained by safety, regulatory, and consumer acceptance factors influencing strain development.

Purpose of the Study:

• To review the Design-Build-Test-Learn (DBTL) framework for developing food-grade microbial platforms.
• To illustrate how DBTL integrates computational design, automated construction, high-throughput testing, and data-driven learning for efficient optimization.

Main Methods:

• Summarizing the integration of computational design and automated strain construction.
• Describing high-throughput testing and data-driven learning within DBTL cycles.
• Presenting case studies in alternative proteins, flavors, and carbohydrates.

Main Results:

• DBTL provides a systematic, iterative approach for rational decision-making in microbial platform development.
• Case studies demonstrate efficient optimization for food relevance and scalability using DBTL workflows.
• The framework supports the development of resilient and sustainable food systems.

Conclusions:

• The DBTL framework unifies synthetic biology with practical food production.
• DBTL-driven workflows are crucial for advancing food-grade precision fermentation.
• This approach facilitates the creation of sustainable and scalable food solutions.