Literature DB >> 10191386

How will bioinformatics influence metabolic engineering?

J S Edwards1, B O Palsson.   

Abstract

Ten microbial genomes have been fully sequenced to date, and the sequencing of many more genomes is expected to be completed before the end of the century. The assignment of function to open reading frames (ORFs) is progressing, and for some genomes over 70% of functional assignments have been made. The majority of the assigned ORFs relate to metabolic functions. Thus, the complete genetic and biochemical functions of a number of microbial cells may be soon available. From a metabolic engineering standpoint, these developments open a new realm of possibilities. Metabolic analysis and engineering strategies can now be built on a sound genomic basis. An important question that now arises; how should these tasks be approached? Flux-balance analysis (FBA) has the potential to play an important role. It is based on the fundamental principle of mass conservation. It requires only the stoichiometric matrix, the metabolic demands, and some strain specific parameters. Importantly, no enzymatic kinetic data is required. In this article, we show how the genomically defined microbial metabolic genotypes can be analyzed by FBA. Fundamental concepts of metabolic genotype, metabolic phenotype, metabolic redundancy and robustness are defined and examples of their use given. We discuss the advantage of this approach, and how FBA is expected to find uses in the near future. FBA is likely to become an important analysis tool for genomically based approaches to metabolic engineering, strain design, and development. Copyright 1998 John Wiley & Sons, Inc.

Mesh:

Year:  1998        PMID: 10191386     DOI: 10.1002/(sici)1097-0290(19980420)58:2/3<162::aid-bit8>3.0.co;2-j

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


  16 in total

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Review 2.  Integration of metabolic reactions and gene regulation.

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3.  The geometry of the flux cone of a metabolic network.

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4.  Analysis of optimality in natural and perturbed metabolic networks.

Authors:  Daniel Segrè; Dennis Vitkup; George M Church
Journal:  Proc Natl Acad Sci U S A       Date:  2002-11-01       Impact factor: 11.205

5.  An algorithm for designing minimal microbial communities with desired metabolic capacities.

Authors:  Alexander Eng; Elhanan Borenstein
Journal:  Bioinformatics       Date:  2016-02-26       Impact factor: 6.937

6.  A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis.

Authors:  Xin Fang; Anders Wallqvist; Jaques Reifman
Journal:  BMC Syst Biol       Date:  2009-09-15

7.  Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis.

Authors:  Carola Huthmacher; Andreas Hoppe; Sascha Bulik; Hermann-Georg Holzhütter
Journal:  BMC Syst Biol       Date:  2010-08-31

8.  Hybrid elementary flux analysis/nonparametric modeling: application for bioprocess control.

Authors:  Ana P Teixeira; Carlos Alves; Paula M Alves; Manuel J T Carrondo; Rui Oliveira
Journal:  BMC Bioinformatics       Date:  2007-01-29       Impact factor: 3.169

9.  Quantifying the metabolic capabilities of engineered Zymomonas mobilis using linear programming analysis.

Authors:  Ivi C Tsantili; M Nazmul Karim; Maria I Klapa
Journal:  Microb Cell Fact       Date:  2007-03-09       Impact factor: 5.328

10.  Use of physiological constraints to identify quantitative design principles for gene expression in yeast adaptation to heat shock.

Authors:  Ester Vilaprinyo; Rui Alves; Albert Sorribas
Journal:  BMC Bioinformatics       Date:  2006-04-03       Impact factor: 3.169

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