Dynamic Metabolomics for Engineering Biology: Accelerating Learning Cycles for Bioproduction.

Journal: Trends in biotechnology
Published Date: Jan 1, 2020

Abstract

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.

Authors

  • Christopher J Vavricka
    Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan.
  • Tomohisa Hasunuma
    Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan; Engineering Biology Research Center, Kobe University, Kobe, Japan.
  • Akihiko Kondo
    Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan; Engineering Biology Research Center, Kobe University, Kobe, Japan; Department of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, Kobe, Japan. Electronic address: akondo@kobe-u.ac.jp.

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