Integration of machine learning and high throughput measurements are essential to drive the next generation of the design-build-test-learn (DBTL) cycle in synthetic biology. Here, we report the use of active learning in combination with metabolomics for optimising production of surfactin, a complex lipopeptide resulting from a non-ribosomal assembly pathway. We designed a media optimisation algorithm that iteratively learns the yield landscape and steers the media composition toward maximal production. The algorithm led to a 160 % yield increase after three DBTL runs as compared to an M9 baseline. Metabolomics data helped to elucidate the underpinning biochemistry for yield improvement and revealed Pareto-like trade-offs in production of other lipopeptides from related pathways. We found positive associations between organic acids and surfactin, suggesting a key role of central carbon metabolism, as well as system-wide anisotropies in how metabolism reacts to shifts in carbon and nitrogen levels. Our framework offers a novel data-driven approach to improve yield of biological products with complex synthesis pathways that are not amenable to traditional yield optimisation strategies.

中文翻译：

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使用数据高效的主动学习和高通量质谱优化芽孢杆菌中的表面活性素产量

机器学习和高通量测量的集成对于推动合成生物学中的下一代设计-构建-测试-学习 (DBTL) 周期至关重要。在这里，我们报告了使用主动学习与代谢组学相结合来优化表面活性素的生产，表面活性素是一种由非核糖体组装途径产生的复杂脂肽。我们设计了一种媒体优化算法，可以迭代地了解产量情况并引导媒体组合实现最大产量。与 M9 基线相比，该算法在运行 3 次 DBTL 后产量提高了 160%。代谢组学数据有助于阐明提高产量的基础生物化学，并揭示了从相关途径生产其他脂肽时的帕累托式权衡。我们发现有机酸和表面活性素之间存在正相关，这表明中心碳代谢的关键作用，以及代谢对碳和氮水平变化的反应的全系统各向异性。我们的框架提供了一种新颖的数据驱动方法，通过复杂的合成途径来提高生物产品的产量，而这些合成途径不适合传统的产量优化策略。