| Literature DB >> 31473013 |
Christopher J Vavricka1, Tomohisa Hasunuma2, Akihiko Kondo3.
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.Keywords: DBTL cycle; bioproduction; learning process; mass spectrometry; metabolic engineering; metabolomics; synthetic biology
Mesh:
Year: 2019 PMID: 31473013 DOI: 10.1016/j.tibtech.2019.07.009
Source DB: PubMed Journal: Trends Biotechnol ISSN: 0167-7799 Impact factor: 19.536