Dynamic Metabolomics for Engineering Biology: Accelerating Learning Cycles for Bioproduction. Issue 1 (January 2020)

• Record Type: Journal Article
• Authors: Vavricka, Christopher J.; Hasunuma, Tomohisa; Kondo, Akihiko
• Is Part Of: Trends in biotechnology. Volume 38:Issue 1(2020)
• Page Start: 68
• Page End: 82
• Publication Date: 2020-01
• Subjects: metabolomics -- mass spectrometry -- metabolic engineering -- synthetic biology -- bioproduction -- DBTL cycle -- learning process; Biotechnology -- Periodicals; Biochemical engineering -- Periodicals; Genetic engineering -- Periodicals; Industrial microbiology -- Periodicals; 660.605
• DOI: 10.1016/j.tibtech.2019.07.009
• Languages: English
• ISSNs: 0167-7799

Abstract

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. Highlights: Mass spectrometry-based metabolomics methods are central to the discovery of secondary metabolites and the metabolic engineering of microbial cell factories. Dynamic metabolomics methods, especially isotope flux experiments, accelerate the rapid discovery of novel metabolic pathways and enzymes to improve synthetic bioproduction pathways. Metabolomics-directed metabolic engineering has enabled the bioproduction of a wide variety of industrial and pharmaceutical chemicals including alcohols, organic acids, aromatics, alkaloids, and terpenoids. Metabolomics drives the test phase of the design, build, test, and learn (DBTL) cycle. However, standardized learning methods must be established for metabolomics-driven synthetic biology. Integration of learning processes together with high-throughput automation and enzyme engineering will expand the range of industrial bioproduction targets.