Literature DB >> 10191381

Metabolic engineering: techniques for analysis of targets for genetic manipulations.

J Nielsen1.   

Abstract

Metabolic engineering has been defined as the purposeful modification of intermediary metabolism using recombinant DNA techniques. With this definition metabolic engineering includes: (1) inserting new pathways in microorganisms with the aim of producing novel metabolites, e.g., production of polyketides by Streptomyces; (2) production of heterologous peptides, e.g., production of human insulin, erythropoitin, and tPA; and (3) improvement of both new and existing processes, e.g., production of antibiotics and industrial enzymes. Metabolic engineering is a multidisciplinary approach, which involves input from chemical engineers, molecular biologists, biochemists, physiologists, and analytical chemists. Obviously, molecular biology is central in the production of novel products, as well as in the improvement of existing processes. However, in the latter case, input from other disciplines is pivotal in order to target the genetic modifications; with the rapid developments in molecular biology, progress in the field is likely to be limited by procedures to identify the optimal genetic changes. Identification of the optimal genetic changes often requires a meticulous mapping of the cellular metabolism at different operating conditions, and the application of metabolic engineering to process optimization is, therefore, expected mainly to have an impact on the improvement of processes where yield, productivity, and titer are important design factors, i.e., in the production of metabolites and industrial enzymes. Despite the prospect of obtaining major improvement through metabolic engineering, this approach is, however, not expected to completely replace the classical approach to strain improvement-random mutagenesis followed by screening. Identification of the optimal genetic changes for improvement of a given process requires analysis of the underlying mechanisms, at best, at the molecular level. To reveal these mechanisms a number of different techniques may be applied: (1) detailed physiological studies, (2) metabolic flux analysis (MFA), (3) metabolic control analysis (MCA), (4) thermodynamic analysis of pathways, and (5) kinetic modeling. In this article, these different techniques are discussed and their applications to the analysis of different processes are illustrated. Copyright 1998 John Wiley & Sons, Inc.

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Year:  1998        PMID: 10191381     DOI: 10.1002/(sici)1097-0290(19980420)58:2/3<125::aid-bit3>3.0.co;2-n

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


  13 in total

1.  Control of the threonine-synthesis pathway in Escherichia coli: a theoretical and experimental approach.

Authors:  C Chassagnole; D A Fell; B Raïs; B Kudla; J P Mazat
Journal:  Biochem J       Date:  2001-06-01       Impact factor: 3.857

2.  An integrated study of threonine-pathway enzyme kinetics in Escherichia coli.

Authors:  C Chassagnole; B Raïs; E Quentin; D A Fell; J P Mazat
Journal:  Biochem J       Date:  2001-06-01       Impact factor: 3.857

3.  Tools for metabolic engineering in Streptomyces.

Authors:  Valerie Bekker; Amanda Dodd; Dean Brady; Karl Rumbold
Journal:  Bioengineered       Date:  2014 Sep-Oct       Impact factor: 3.269

4.  Coupling the CRISPR/Cas9 System with Lambda Red Recombineering Enables Simplified Chromosomal Gene Replacement in Escherichia coli.

Authors:  Michael E Pyne; Murray Moo-Young; Duane A Chung; C Perry Chou
Journal:  Appl Environ Microbiol       Date:  2015-05-22       Impact factor: 4.792

Review 5.  Expanding the concepts and tools of metabolic engineering to elucidate cancer metabolism.

Authors:  Mark A Keibler; Sarah-Maria Fendt; Gregory Stephanopoulos
Journal:  Biotechnol Prog       Date:  2012-10-18

6.  Metabolic flexibility of D-ribose producer strain of Bacillus pumilus under environmental perturbations.

Authors:  Rajesh K Srivastava; Soumen K Maiti; Debasish Das; Prashant M Bapat; Kritika Batta; Mani Bhushan; Pramod P Wangikar
Journal:  J Ind Microbiol Biotechnol       Date:  2012-03-22       Impact factor: 3.346

7.  CBFA: phenotype prediction integrating metabolic models with constraints derived from experimental data.

Authors:  Rafael Carreira; Pedro Evangelista; Paulo Maia; Paulo Vilaça; Marcellinus Pont; Jean-François Tomb; Isabel Rocha; Miguel Rocha
Journal:  BMC Syst Biol       Date:  2014-12-03

8.  Carbon flux analysis in a pantothenate overproducing Corynebacterium glutamicum strain.

Authors:  Christophe Chassagnole; Fabien Létisse; Audrey Diano; Nic D Lindley
Journal:  Mol Biol Rep       Date:  2002       Impact factor: 2.316

Review 9.  Effects of hypoglycaemia on neuronal metabolism in the adult brain: role of alternative substrates to glucose.

Authors:  Ana I Amaral
Journal:  J Inherit Metab Dis       Date:  2012-10-30       Impact factor: 4.982

10.  A combined computational-experimental analyses of selected metabolic enzymes in Pseudomonas species.

Authors:  Deepak Perumal; Chu Sing Lim; Vincent T K Chow; Kishore R Sakharkar; Meena K Sakharkar
Journal:  Int J Biol Sci       Date:  2008-09-10       Impact factor: 6.580

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