Your browser doesn't support javascript.
loading
Stoichiometric capacitance reveals the theoretical capabilities of metabolic networks.
Larhlimi, Abdelhalim; Basler, Georg; Grimbs, Sergio; Selbig, Joachim; Nikoloski, Zoran.
Afiliação
  • Larhlimi A; Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, D-14476 Potsdam, Germany. larhlimi@mpimp-golm.mpg.de
Bioinformatics ; 28(18): i502-i508, 2012 Sep 15.
Article em En | MEDLINE | ID: mdl-22962473
ABSTRACT
MOTIVATION Metabolic engineering aims at modulating the capabilities of metabolic networks by changing the activity of biochemical reactions. The existing constraint-based approaches for metabolic engineering have proven useful, but are limited only to reactions catalogued in various pathway databases.

RESULTS:

We consider the alternative of designing synthetic strategies which can be used not only to characterize the maximum theoretically possible product yield but also to engineer networks with optimal conversion capability by using a suitable biochemically feasible reaction called 'stoichiometric capacitance'. In addition, we provide a theoretical solution for decomposing a given stoichiometric capacitance over a set of known enzymatic reactions. We determine the stoichiometric capacitance for genome-scale metabolic networks of 10 organisms from different kingdoms of life and examine its implications for the alterations in flux variability patterns. Our empirical findings suggest that the theoretical capacity of metabolic networks comes at a cost of dramatic system's changes. CONTACT larhlimi@mpimp-golm.mpg.de, or nikoloski@mpimp-golm.mpg.de SUPPLEMENTARY INFORMATION Supplementary tables are available at Bioinformatics online.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes e Vias Metabólicas / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2012 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes e Vias Metabólicas / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2012 Tipo de documento: Article