Biomanufacturing could contribute as much as $30 trillion to the global economy by 2030. But the success of the growing bioeconomy depends on our ability to manufacture high-performing strains in a time- and cost-effective manner. The Design-Build-Test-Learn (DBTL) framework has proven to be an effective strain engineering approach. Significant improvements have been made in genome engineering, genotyping, and phenotyping throughput over the last couple of decades that have greatly accelerated the DBTL cycles. However, to achieve a radical reduction in strain development time and cost, we need to look at the strain engineering process through a lens of optimizing the whole cycle, as opposed to simply increasing throughput at each stage. We propose an approach that integrates all four stages of the DBTL cycle and takes advantage of the advances in computational design, high-throughput genome engineering, and phenotyping methods, as well as machine learning tools for making predictions about strain scaleup performance. In this perspective, we discuss the challenges of industrial strain engineering, outline the best approaches to overcoming these challenges, and showcase examples of successful strain engineering projects for production of heterologous proteins, amino acids, and small molecules, as well as improving tolerance, fitness, and de-risking the scaleup of industrial strains.

中文翻译：

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优化生物基分子工业规模生产的菌株工程工艺

到 2030 年，生物制造可为全球经济贡献高达 30 万亿美元。但不断增长的生物经济的成功取决于我们以时间和成本效益高的方式制造高性能菌株的能力。设计-构建-测试-学习 (DBTL) 框架已被证明是一种有效的应变工程方法。在过去的几十年里，基因组工程、基因分型和表型分析通量取得了显着的进步，极大地加快了 DBTL 周期。然而，为了从根本上减少应变开发时间和成本，我们需要通过优化整个周期的角度来看待应变工程过程，而不是简单地增加每个阶段的吞吐量。我们提出了一种集成 DBTL 循环的所有四个阶段的方法，并利用计算设计、高通量基因组工程和表型分析方法以及机器学习工具的进步来预测菌株放大性能。从这个角度来看，我们讨论了工业菌株工程的挑战，概述了克服这些挑战的最佳方法，并展示了用于生产异源蛋白质、氨基酸和小分子以及提高耐受性、适应性的成功菌株工程项目的示例，并降低工业菌株规模化的风险。