Vavricka Christopher J, Hasunuma Tomohisa, Kondo Akihiko
Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan.
Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan; Engineering Biology Research Center, Kobe University, Kobe, Japan.
Trends Biotechnol. 2020 Jan;38(1):68-82. doi: 10.1016/j.tibtech.2019.07.009. Epub 2019 Aug 28.
Metabolomics is a powerful tool to rationally guide the metabolic engineering of synthetic bioproduction pathways. Current reports indicate great potential to further develop metabolomics-directed synthetic bioproduction. Advanced mass metabolomics methods including isotope flux analysis, untargeted metabolomics, and system-wide approaches are assisting the characterization of metabolic pathways and enabling the biosynthesis of more complex products. More importantly, a design, build, test, and learn (DBTL) cycle is accelerating synthetic biology research and is highly compatible with metabolomics data to further expand bioproduction capability. However, learning processes are currently the weakest link in this workflow. Therefore, guidelines for the development of metabolic learning processes are proposed based on bioproduction examples. Linking dynamic mass spectrometry (MS) methodologies together with automated learning workflows is encouraged.
代谢组学是合理指导合成生物生产途径代谢工程的有力工具。当前报告表明，进一步发展代谢组学导向的合成生物生产具有巨大潜力。先进的大规模代谢组学方法，包括同位素通量分析、非靶向代谢组学和全系统方法，正在辅助代谢途径的表征，并实现更复杂产品的生物合成。更重要的是，设计、构建、测试和学习（DBTL）循环正在加速合成生物学研究，并且与代谢组学数据高度兼容，以进一步扩大生物生产能力。然而，学习过程目前是该工作流程中最薄弱的环节。因此，基于生物生产实例提出了代谢学习过程的发展指南。鼓励将动态质谱（MS）方法与自动化学习工作流程相结合。
