scholarly journals Effects of metabolic engineering on downstream processing operational cost and energy consumption: the case of Escherichia coli 's glycerol conversion to succinic acid

2017 ◽  
Vol 93 (7) ◽  
pp. 2011-2020 ◽  
Author(s):  
Albert Enrique Tafur Rangel ◽  
Laura Carolina Camelo Valera ◽  
Jorge Mario Gómez Ramírez ◽  
Andrés Fernando González Barrios
Keyword(s):  
Escherichia Coli ◽  
Energy Consumption ◽  
Metabolic Engineering ◽  
Succinic Acid ◽  
Downstream Processing ◽  
Operational Cost ◽  
Glycerol Conversion

scholarly journals In Silico Design for Systems-Based Metabolic Engineering In Escherichia Coli for The Bioconversion of Aerobic Glycerol to Succinic Acid

2020 ◽  
Author(s):  
Albert Enrique Tafur Rangel ◽  
Wendy Lorena Rios Guzman ◽  
Carmen Elvira Ojeda Cuella ◽  
Daissy Esther Mejia Perez ◽  
Ross Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Succinic Acid ◽  
In Silico ◽  
Rational Design ◽  
Industrial Biotechnology ◽  
E Coli ◽  
Cell Factories ◽  
Transcriptomics Data

Abstract BackgroundGlycerol has become an interesting carbon source for industrial processes as consequence of the biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. Selecting the appropriate metabolic targets to build efficient cell factories and maximize the desired chemical production in as little time as possible is a major challenge in industrial biotechnology. The engineering of microbial metabolism following rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, to be proficient is needed known in advance the effects of gene deletions.ResultsAn in silico experiment was performed to model and understand the effects of metabolic engineering over the metabolism by transcriptomics data integration. In this study, systems-based metabolic engineering to predict the metabolic engineering targets was used in order to increase the bioconversion of glycerol to succinic acid by Escherichia coli. Transcriptomics analysis suggest insights of how increase the glycerol utilization of the cell for further design efficient cell factories. Three models were used; an E. coli core model, a model obtained after the integration of transcriptomics data obtained from E. coli growing in an optimized culture media, and a third one obtained after integration of transcriptomics data obtained from E. coli after adaptive laboratory evolution experiments. A total of 2402 strains were obtained from these three models. Fumarase and pyruvate dehydrogenase were frequently predicted in all the models, suggesting that these reactions are essential to increasing succinic acid production from glycerol. Finally, using flux balance analysis results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of importance of each knockout’s (feature’s) contribution.ConclusionsThe combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed versatile molecular mechanisms involved in the utilization of glycerol. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work a guide platform for the selection/engineering of microorganisms for production of interesting chemical compounds.

Metabolic engineering of Escherichia coli for the production of succinic acid from glucose

2013 ◽  
Vol 49 (7) ◽  
pp. 629-637 ◽  
Author(s):  
A. Yu. Skorokhodova ◽  
A. Yu. Gulevich ◽  
A. A. Morzhakova ◽  
R. S. Shakulov ◽  
V. G. Debabov
Keyword(s):  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Succinic Acid

Metabolic Engineering of Escherichia coli for High Yield Production of Succinic Acid Driven by Methanol

2018 ◽  
Vol 7 (12) ◽  
pp. 2803-2811 ◽  
Author(s):  
Wenming Zhang ◽  
Ting Zhang ◽  
Meng Song ◽  
Zhongxue Dai ◽  
Shangjie Zhang ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Succinic Acid ◽  
High Yield ◽  
Yield Production ◽  
High Yield Production

Creation of Plasmidless and Markerless Escherichia coli Succinate Producing Strain and Evaluation of its Biosynthetic Potential during Utilization of Lignocellulosic Sugars

2020 ◽  
Vol 36 (2) ◽  
pp. 3-11
Author(s):  
O.A. Zhuravliova ◽  
Т.А. Voeikova ◽  
A.Yu. Gulevich ◽  
V.G. Debabov
Keyword(s):  
Escherichia Coli ◽  
Succinic Acid ◽  
Plant Biomass ◽  
Anaerobic Fermentation ◽  
Basic Research ◽  
Acid Fermentation ◽  
Mixed Acid ◽  
Gene Coding ◽  
Lignocellulosic Sugars ◽  
Basic Research Project

The plasmidless and markerless Escherichia coli succinate producing strain SGM2.0Pyc-int has been engineered and characterized. The strain has the inactivated main mixed-acid fermentation pathways due to the deletions of ldhA,poxB, ackA,pta, and adhE genes, constitutively expresses the genes of the aceEF-lpdA operon encoding components of pyravate dehydrogenase complex, and possesses the chromosomally integrated Bacillus subtilis pycA gene coding for pyruvate carboxylase. The capacity of the strain to synthesize succinic acid in course of dual-phase aerobic-anaerobic fermentation with lignocellulosic sugars as substrates was studied. The SGM2.0Pyc-int strain synthesized succinic acid from glucose, xylose, and arabinose with a molar yields of 1.41 mol/mol, 1.18 mol/mol, and 1.18 mol/mol, respectively, during the anaerobic production stage. The constructed strain has great potential for developing efficient processes for the succinic acid production from plant biomass-derived sugars. Escherichia coli, fermentation, arabinose, glucose, xylose, succinic acid. The work was supported by a Grant from the Russian Foundation for Basic Research (Project no. 18-29-14005).

Metabolic engineering of Escherichia coli for the production of malic acid

2008 ◽  
Vol 40 (2) ◽  
pp. 312-320 ◽  
Author(s):  
Soo Yun Moon ◽  
Soon Ho Hong ◽  
Tae Yong Kim ◽  
Sang Yup Lee
Keyword(s):  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Malic Acid
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