Metabolic engineering of Escherichia coli W3110 strain by incorporating genome-level modifications and synthetic plasmid modules to enhance L-Dopa production from glycerol

2018 ◽  
Vol 48 (8) ◽  
pp. 671-682 ◽  
Author(s):  
Arunangshu Das ◽  
Neetu Tyagi ◽  
Anita Verma ◽  
Sarfaraz Akhtar ◽  
Krishna J. Mukherjee
2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Mee K. Lee ◽  
Mohd Saberi Mohamad ◽  
Yee Wen Choon ◽  
Kauthar Mohd Daud ◽  
Nurul Athirah Nasarudin ◽  
...  

AbstractThe metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite’s production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.


2008 ◽  
Vol 40 (2) ◽  
pp. 312-320 ◽  
Author(s):  
Soo Yun Moon ◽  
Soon Ho Hong ◽  
Tae Yong Kim ◽  
Sang Yup Lee

2017 ◽  
Vol 241 ◽  
pp. 430-438 ◽  
Author(s):  
Chonglong Wang ◽  
Bakht Zada ◽  
Gongyuan Wei ◽  
Seon-Won Kim

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhenning Liu ◽  
Xue Zhang ◽  
Dengwei Lei ◽  
Bin Qiao ◽  
Guang-Rong Zhao

Abstract Background 3-Phenylpropanol with a pleasant odor is widely used in foods, beverages and cosmetics as a fragrance ingredient. It also acts as the precursor and reactant in pharmaceutical and chemical industries. Currently, petroleum-based manufacturing processes of 3-phenypropanol is environmentally unfriendly and unsustainable. In this study, we aim to engineer Escherichia coli as microbial cell factory for de novo production of 3-phenypropanol via retrobiosynthesis approach. Results Aided by in silico retrobiosynthesis analysis, we designed a novel 3-phenylpropanol biosynthetic pathway extending from l-phenylalanine and comprising the phenylalanine ammonia lyase (PAL), enoate reductase (ER), aryl carboxylic acid reductase (CAR) and phosphopantetheinyl transferase (PPTase). We screened the enzymes from plants and microorganisms and reconstructed the artificial pathway for conversion of 3-phenylpropanol from l-phenylalanine. Then we conducted chromosome engineering to increase the supply of precursor l-phenylalanine and combined the upstream l-phenylalanine pathway and downstream 3-phenylpropanol pathway. Finally, we regulated the metabolic pathway strength and optimized fermentation conditions. As a consequence, metabolically engineered E. coli strain produced 847.97 mg/L of 3-phenypropanol at 24 h using glucose-glycerol mixture as co-carbon source. Conclusions We successfully developed an artificial 3-phenylpropanol pathway based on retrobiosynthesis approach, and highest titer of 3-phenylpropanol was achieved in E. coli via systems metabolic engineering strategies including enzyme sources variety, chromosome engineering, metabolic strength balancing and fermentation optimization. This work provides an engineered strain with industrial potential for production of 3-phenylpropanol, and the strategies applied here could be practical for bioengineers to design and reconstruct the microbial cell factory for high valuable chemicals.


2021 ◽  
pp. 126050
Author(s):  
Pei Wang ◽  
Hai-Yan Zhou ◽  
Bo Li ◽  
Wen-Qing Ding ◽  
Zhi-Qiang Liu ◽  
...  

2017 ◽  
Vol 9 (10) ◽  
pp. 830-835 ◽  
Author(s):  
Xingxing Jian ◽  
Ningchuan Li ◽  
Qian Chen ◽  
Qiang Hua

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed.


2017 ◽  
Vol 184 (2) ◽  
pp. 703-715
Author(s):  
Ting Jiang ◽  
Chen Zhang ◽  
Qin He ◽  
Zhaojuan Zheng ◽  
Jia Ouyang

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