Literature DB >> 12209828

Inverse metabolic engineering: a strategy for directed genetic engineering of useful phenotypes.

James E Bailey1, Adriana Sburlati, Vassily Hatzimanikatis, Kelvin Lee, Wolfgang A Renner, Philip S Tsai.   

Abstract

The classical method of metabolic engineering, identifying a rate-determining step in a pathway and alleviating the bottleneck by enzyme overexpression, has motivated much research but has enjoyed only limited practical success. Intervention of other limiting steps, of counter-balancing regulation, and of unknown coupled pathways often confounds this direct approach. Here the concept of inverse metabolic engineering is codified and its application is illustrated with several examples. Inverse metabolic engineering means the elucidation of a metabolic engineering strategy by: first, identifying, constructing, or calculating a desired phenotype; second, determining the genetic or the particular environmental factors conferring that phenotype; and third, endowing that phenotype on another strain or organism by directed genetic or environmental manipulation. This paradigm has been successfully applied in several contexts, including elimination of growth factor requirements in mammalian cell culture and increasing the energetic efficiency of microaerobic bacterial respiration. Copyright 2002 Wiley Periodicals, Inc.

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Year:  2002        PMID: 12209828     DOI: 10.1002/bit.10441

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  15 in total

1.  Enhancement of biodesulfurization in two-liquid systems by heterogeneous expression of vitreoscilla hemoglobin.

Authors:  Xiaochao Xiong; Jianmin Xing; Xin Li; Xuejing Bai; Wangliang Li; Yuguang Li; Huizhou Liu
Journal:  Appl Environ Microbiol       Date:  2007-02-09       Impact factor: 4.792

Review 2.  Cells by design: a mini-review of targeting cell engineering using DNA microarrays.

Authors:  Pratik Jaluria; Chia Chu; Michael Betenbaugh; Joseph Shiloach
Journal:  Mol Biotechnol       Date:  2008-06       Impact factor: 2.695

3.  Using cell engineering and omic tools for the improvement of cell culture processes.

Authors:  Darrin Kuystermans; Britta Krampe; Halina Swiderek; Mohamed Al-Rubeai
Journal:  Cytotechnology       Date:  2007-02-24       Impact factor: 2.058

4.  Resistance of Saccharomyces cerevisiae to high concentrations of furfural is based on NADPH-dependent reduction by at least two oxireductases.

Authors:  Dominik Heer; Daniel Heine; Uwe Sauer
Journal:  Appl Environ Microbiol       Date:  2009-10-23       Impact factor: 4.792

5.  Improvement of xylose uptake and ethanol production in recombinant Saccharomyces cerevisiae through an inverse metabolic engineering approach.

Authors:  Yong-Su Jin; Hal Alper; Yea-Tyng Yang; Gregory Stephanopoulos
Journal:  Appl Environ Microbiol       Date:  2005-12       Impact factor: 4.792

Review 6.  Rapid prototyping of microbial cell factories via genome-scale engineering.

Authors:  Tong Si; Han Xiao; Huimin Zhao
Journal:  Biotechnol Adv       Date:  2014-11-20       Impact factor: 14.227

Review 7.  Improving industrial yeast strains: exploiting natural and artificial diversity.

Authors:  Jan Steensels; Tim Snoek; Esther Meersman; Martina Picca Nicolino; Karin Voordeckers; Kevin J Verstrepen
Journal:  FEMS Microbiol Rev       Date:  2014-05-08       Impact factor: 16.408

Review 8.  Progress in metabolic engineering of Saccharomyces cerevisiae.

Authors:  Elke Nevoigt
Journal:  Microbiol Mol Biol Rev       Date:  2008-09       Impact factor: 11.056

9.  GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network.

Authors:  Dan Jacobson; Guy Emerton
Journal:  BMC Bioinformatics       Date:  2012-08-09       Impact factor: 3.169

10.  NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.

Authors:  Matthew C Schmidt; Andrea M Rocha; Kanchana Padmanabhan; Yekaterina Shpanskaya; Jill Banfield; Kathleen Scott; James R Mihelcic; Nagiza F Samatova
Journal:  PLoS Comput Biol       Date:  2012-05-10       Impact factor: 4.475

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