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Cellular determinants of metabolite concentration ranges.
Küken, Anika; Eloundou-Mbebi, Jeanne M O; Basler, Georg; Nikoloski, Zoran.
Afiliación
  • Küken A; System Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
  • Eloundou-Mbebi JMO; Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
  • Basler G; System Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
  • Nikoloski Z; Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
PLoS Comput Biol ; 15(1): e1006687, 2019 01.
Article en En | MEDLINE | ID: mdl-30677015
ABSTRACT
Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component's concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Biología de Sistemas / Redes y Vías Metabólicas / Metaboloma / Modelos Biológicos Tipo de estudio: Prognostic_studies Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Biología de Sistemas / Redes y Vías Metabólicas / Metaboloma / Modelos Biológicos Tipo de estudio: Prognostic_studies Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Alemania