Literature DB >> 31473013

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

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.
Copyright © 2019 Elsevier Ltd. All rights reserved.

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


  2 in total

Review 1.  Emerging Trends in Genetic Engineering of Microalgae for Commercial Applications.

Authors:  Samir B Grama; Zhiyuan Liu; Jian Li
Journal:  Mar Drugs       Date:  2022-04-24       Impact factor: 6.085

2.  Machine learning discovery of missing links that mediate alternative branches to plant alkaloids.

Authors:  Christopher J Vavricka; Shunsuke Takahashi; Naoki Watanabe; Musashi Takenaka; Mami Matsuda; Takanobu Yoshida; Ryo Suzuki; Hiromasa Kiyota; Jianyong Li; Hiromichi Minami; Jun Ishii; Kenji Tsuge; Michihiro Araki; Akihiko Kondo; Tomohisa Hasunuma
Journal:  Nat Commun       Date:  2022-03-16       Impact factor: 17.694

  2 in total

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