Literature DB >> 11101318

Robustness analysis of the Escherichia coli metabolic network.

J S Edwards1, B O Palsson.   

Abstract

Genomic, biochemical, and strain-specific data can be assembled to define an in silico representation of the metabolic network for a select group of single cellular organisms. Flux-balance analysis and phenotypic phase planes derived therefrom have been developed and applied to analyze the metabolic capabilities and characteristics of Escherichia coli K-12. These analyses have shown the existence of seven essential reactions in the central metabolic pathways (glycolysis, pentose phosphate pathway, tricarboxylic acid cycle) for the growth in glucose minimal media. The corresponding seven gene products can be grouped into three categories: (1) pentose phosphate pathway genes, (2) three-carbon glycolytic genes, and (3) tricarboxylic acid cycle genes. Here we develop a procedure that calculates the sensitivity of optimal cellular growth to altered flux levels of these essential gene products. The results indicate that the E. coli metabolic network is robust with respect to the flux levels of these enzymes. The metabolic flux in the transketolase and the tricarboxylic acid cycle reactions can be reduced to 15% and 19%, respectively, of the optimal value without significantly influencing the optimal growth flux. The metabolic network also exhibited robustness with respect to the ribose-5-phosphate isomerase, and the ribose-5-phosephate isomerase flux was reduced to 28% of the optimal value without significantly effecting the optimal growth flux. The metabolic network exhibited limited robustness to the three-carbon glycolytic fluxes both increased and decreased. The development presented another dimension to the use of FBA to study the capabilities of metabolic networks.

Entities:  

Mesh:

Substances:

Year:  2000        PMID: 11101318     DOI: 10.1021/bp0000712

Source DB:  PubMed          Journal:  Biotechnol Prog        ISSN: 1520-6033


  67 in total

1.  Energy balance for analysis of complex metabolic networks.

Authors:  Daniel A Beard; Shou-dan Liang; Hong Qian
Journal:  Biophys J       Date:  2002-07       Impact factor: 4.033

2.  Superessential reactions in metabolic networks.

Authors:  Aditya Barve; João Frederico Matias Rodrigues; Andreas Wagner
Journal:  Proc Natl Acad Sci U S A       Date:  2012-04-16       Impact factor: 11.205

Review 3.  A metabolic network approach for the identification and prioritization of antimicrobial drug targets.

Authors:  Arvind K Chavali; Kevin M D'Auria; Erik L Hewlett; Richard D Pearson; Jason A Papin
Journal:  Trends Microbiol       Date:  2012-01-31       Impact factor: 17.079

4.  Adaptive Genetic Robustness of Escherichia coli Metabolic Fluxes.

Authors:  Wei-Chin Ho; Jianzhi Zhang
Journal:  Mol Biol Evol       Date:  2016-01-05       Impact factor: 16.240

5.  Circuit topology and the evolution of robustness in two-gene circadian oscillators.

Authors:  Andreas Wagner
Journal:  Proc Natl Acad Sci U S A       Date:  2005-08-08       Impact factor: 11.205

6.  Divergence and redundancy of transport and metabolic rate-yield strategies in a single Escherichia coli population.

Authors:  Ram Prasad Maharjan; Shona Seeto; Thomas Ferenci
Journal:  J Bacteriol       Date:  2006-12-08       Impact factor: 3.490

7.  Evolution of complexity in signaling pathways.

Authors:  Orkun S Soyer; Sebastian Bonhoeffer
Journal:  Proc Natl Acad Sci U S A       Date:  2006-10-23       Impact factor: 11.205

8.  A network-based method for target selection in metabolic networks.

Authors:  R Guimerà; M Sales-Pardo; L A N Amaral
Journal:  Bioinformatics       Date:  2007-04-26       Impact factor: 6.937

9.  Topological signatures of species interactions in metabolic networks.

Authors:  Elhanan Borenstein; Marcus W Feldman
Journal:  J Comput Biol       Date:  2009-02       Impact factor: 1.479

10.  Aerobic fermentation of D-glucose by an evolved cytochrome oxidase-deficient Escherichia coli strain.

Authors:  Vasiliy A Portnoy; Markus J Herrgård; Bernhard Ø Palsson
Journal:  Appl Environ Microbiol       Date:  2008-10-24       Impact factor: 4.792

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.