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

Precision fermentation enables the sustainable synthesis of value-added products by engineering microorganisms into specialized cell factories. Unlike traditional fermentation, food-grade precision fermentation operates within a highly regulated framework related to safety considerations, regulatory approval processes, and consumer acceptance, which strongly influence strain selection and engineering strategies. This review explores the Design-Build-Test-Learn (DBTL) framework as a systematic, iterative approach for developing these food-grade microbial platforms. We summarize how computational design, automated strain construction, high-throughput testing, and data-driven learning are integrated to guide rational decision-making across DBTL cycles. Representative case studies in alternative proteins, flavor compounds, and functional carbohydrates illustrate how DBTL-driven workflows enable efficient optimization while maintaining food relevance and scalability. Together, these examples highlight DBTL as a unifying framework that bridges advanced synthetic biology with practical food production and supports the development of resilient and sustainable food systems.