Scholarlink

<p>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&#xa0;considerations, regulatory approval&#xa0;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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2026年3月20日

查看全文
  • Jonghyeok Shin

摘要

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.