Literature DB >> 14571390

In silico metabolic pathway analysis and design: succinic acid production by metabolically engineered Escherichia coli as an example.

Sang Yup Lee1, Soon Ho Hong, Soo Yun Moon.   

Abstract

The intracellular metabolic fluxes can be calculated by metabolic flux analysis, which uses a stoichiometric model for the intracellular reactions along with mass balances around the intracellular metabolites. In this study, we have constructed in silico metabolic pathway network of Escherichia coli consisting of 301 reactions and 294 metabolites. Metabolic flux analyses were carried out to estimate flux distributions to achieve the maximum in silico yield of succinic acid in E. coli. The maximum in silico yield of succinic acid was only 83% of its theoretical yield. The lower in silico yield of succinic acid was found to be due to the insufficient reducing power, which could be increased to its theoretical yield by supplying more reducing power. Furthermore, the optimal metabolic pathways for the production of succinic acid could be proposed based on the results of metabolic flux analyses. In the case of succinic acid production, it was found that pyruvate carboxylation pathway should be used rather than phosphoenolpyruvate carboxylation pathway for its optimal production in E. coli. Then, the in silico optimal succinic acid pathway was compared with conventional succinic acid pathway through minimum set of wet experiments. The results of wet experiments indicate that the pathway predicted by in silico analysis is more efficient than conventional pathway.

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Year:  2002        PMID: 14571390

Source DB:  PubMed          Journal:  Genome Inform        ISSN: 0919-9454


  9 in total

1.  Effects of eliminating pyruvate node pathways and of coexpression of heterogeneous carboxylation enzymes on succinate production by Enterobacter aerogenes.

Authors:  Yoshinori Tajima; Yoko Yamamoto; Keita Fukui; Yousuke Nishio; Kenichi Hashiguchi; Yoshihiro Usuda; Koji Sode
Journal:  Appl Environ Microbiol       Date:  2014-11-21       Impact factor: 4.792

Review 2.  Systems metabolic engineering: genome-scale models and beyond.

Authors:  John Blazeck; Hal Alper
Journal:  Biotechnol J       Date:  2010-07       Impact factor: 4.677

Review 3.  Genome-scale modeling for metabolic engineering.

Authors:  Evangelos Simeonidis; Nathan D Price
Journal:  J Ind Microbiol Biotechnol       Date:  2015-01-13       Impact factor: 3.346

4.  OptFlux: an open-source software platform for in silico metabolic engineering.

Authors:  Isabel Rocha; Paulo Maia; Pedro Evangelista; Paulo Vilaça; Simão Soares; José P Pinto; Jens Nielsen; Kiran R Patil; Eugénio C Ferreira; Miguel Rocha
Journal:  BMC Syst Biol       Date:  2010-04-19

5.  OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions.

Authors:  Sridhar Ranganathan; Patrick F Suthers; Costas D Maranas
Journal:  PLoS Comput Biol       Date:  2010-04-15       Impact factor: 4.475

Review 6.  Bridging the gap between fluxomics and industrial biotechnology.

Authors:  Xueyang Feng; Lawrence Page; Jacob Rubens; Lauren Chircus; Peter Colletti; Himadri B Pakrasi; Yinjie J Tang
Journal:  J Biomed Biotechnol       Date:  2011-01-02

Review 7.  Metabolic engineering of biocatalysts for carboxylic acids production.

Authors:  Ping Liu; Laura R Jarboe
Journal:  Comput Struct Biotechnol J       Date:  2012-11-12       Impact factor: 7.271

8.  Natural computation meta-heuristics for the in silico optimization of microbial strains.

Authors:  Miguel Rocha; Paulo Maia; Rui Mendes; José P Pinto; Eugénio C Ferreira; Jens Nielsen; Kiran Raosaheb Patil; Isabel Rocha
Journal:  BMC Bioinformatics       Date:  2008-11-27       Impact factor: 3.169

Review 9.  Improved succinate production by metabolic engineering.

Authors:  Ke-Ke Cheng; Gen-Yu Wang; Jing Zeng; Jian-An Zhang
Journal:  Biomed Res Int       Date:  2013-04-18       Impact factor: 3.411

  9 in total

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