Literature DB >> 26996613

Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications.

Gang Wu1, Qiang Yan2, J Andrew Jones3, Yinjie J Tang4, Stephen S Fong5, Mattheos A G Koffas6.   

Abstract

Engineering cell metabolism for bioproduction not only consumes building blocks and energy molecules (e.g., ATP) but also triggers energetic inefficiency inside the cell. The metabolic burdens on microbial workhorses lead to undesirable physiological changes, placing hidden constraints on host productivity. We discuss cell physiological responses to metabolic burdens, as well as strategies to identify and resolve the carbon and energy burden problems, including metabolic balancing, enhancing respiration, dynamic regulatory systems, chromosomal engineering, decoupling cell growth with production phases, and co-utilization of nutrient resources. To design robust strains with high chances of success in industrial settings, novel genome-scale models (GSMs), (13)C-metabolic flux analysis (MFA), and machine-learning approaches are needed for weighting, standardizing, and predicting metabolic costs.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  (13)C-MFA; chromosomal engineering; genome-scale model; machine learning

Mesh:

Substances:

Year:  2016        PMID: 26996613     DOI: 10.1016/j.tibtech.2016.02.010

Source DB:  PubMed          Journal:  Trends Biotechnol        ISSN: 0167-7799            Impact factor:   19.536


  123 in total

1.  Synthetic microbial consortia for biosynthesis and biodegradation: promises and challenges.

Authors:  Shun Che; Yujie Men
Journal:  J Ind Microbiol Biotechnol       Date:  2019-07-05       Impact factor: 3.346

Review 2.  Optimization of industrial microorganisms: recent advances in synthetic dynamic regulators.

Authors:  Byung Eun Min; Hyun Gyu Hwang; Hyun Gyu Lim; Gyoo Yeol Jung
Journal:  J Ind Microbiol Biotechnol       Date:  2016-11-10       Impact factor: 3.346

Review 3.  Emerging strategies for engineering microbial communities.

Authors:  Ryan Tsoi; Zhuojun Dai; Lingchong You
Journal:  Biotechnol Adv       Date:  2019-03-15       Impact factor: 14.227

4.  Engineering of a Highly Efficient Escherichia coli Strain for Mevalonate Fermentation through Chromosomal Integration.

Authors:  Jilong Wang; Suthamat Niyompanich; Yi-Shu Tai; Jingyu Wang; Wenqin Bai; Prithviraj Mahida; Tuo Gao; Kechun Zhang
Journal:  Appl Environ Microbiol       Date:  2016-11-21       Impact factor: 4.792

Review 5.  Understanding D-xylonic acid accumulation: a cornerstone for better metabolic engineering approaches.

Authors:  Angelo B Bañares; Grace M Nisola; Kris Niño G Valdehuesa; Won-Keun Lee; Wook-Jin Chung
Journal:  Appl Microbiol Biotechnol       Date:  2021-07-03       Impact factor: 4.813

6.  Synthetic microbial consortia enable rapid assembly of pure translation machinery.

Authors:  Fernando Villarreal; Luis E Contreras-Llano; Michael Chavez; Yunfeng Ding; Jinzhen Fan; Tingrui Pan; Cheemeng Tan
Journal:  Nat Chem Biol       Date:  2017-11-13       Impact factor: 15.040

7.  Metabolic division of labor in microbial systems.

Authors:  Ryan Tsoi; Feilun Wu; Carolyn Zhang; Sharon Bewick; David Karig; Lingchong You
Journal:  Proc Natl Acad Sci U S A       Date:  2018-02-20       Impact factor: 11.205

8.  Production of 1-octanol in Escherichia coli by a high flux thioesterase route.

Authors:  Néstor J Hernández Lozada; Trevor R Simmons; Ke Xu; Michael A Jindra; Brian F Pfleger
Journal:  Metab Eng       Date:  2020-07-22       Impact factor: 9.783

9.  Developing a pyruvate-driven metabolic scenario for growth-coupled microbial production.

Authors:  Jian Wang; Ruihua Zhang; Yan Zhang; Yaping Yang; Yuheng Lin; Yajun Yan
Journal:  Metab Eng       Date:  2019-07-23       Impact factor: 9.783

10.  Production of optically pure 2,3-butanediol from Miscanthus floridulus hydrolysate using engineered Bacillus licheniformis strains.

Authors:  Yabin Gao; Huahua Huang; Shouwen Chen; Gaofu Qi
Journal:  World J Microbiol Biotechnol       Date:  2018-04-23       Impact factor: 3.312

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