Modeling Leucine’s Metabolic Pathway and ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
Modeling Leucine’s Metabolic Pathway and Knockout Prediction Improving the Production of Surfactin, a Biosurfactant from Bacillus Subtilis
Auteur(s) :
Coutte, Francois [Auteur]
Laboratoire de Procédés Biologiques, Génie Enzymatique et Microbien [ProBioGEM]
Institut Charles Viollette (ICV) - EA 7394 [ICV]
Niehren, Joachim [Auteur]
Linking Dynamic Data [LINKS]
BioComputing
Dhali, Debarun [Auteur]
Laboratoire de Procédés Biologiques, Génie Enzymatique et Microbien [ProBioGEM]
Institut Charles Viollette (ICV) - EA 7394 [ICV]
John, Mathias [Auteur]
BioComputing
Versari, Cristian [Auteur]
BioComputing
Jacques, Philippe [Auteur]
Institut Charles Viollette (ICV) - EA 7394 [ICV]
Laboratoire de Procédés Biologiques, Génie Enzymatique et Microbien [ProBioGEM]
Laboratoire de Procédés Biologiques, Génie Enzymatique et Microbien [ProBioGEM]
Institut Charles Viollette (ICV) - EA 7394 [ICV]
Niehren, Joachim [Auteur]
Linking Dynamic Data [LINKS]
BioComputing
Dhali, Debarun [Auteur]
Laboratoire de Procédés Biologiques, Génie Enzymatique et Microbien [ProBioGEM]
Institut Charles Viollette (ICV) - EA 7394 [ICV]
John, Mathias [Auteur]
BioComputing
Versari, Cristian [Auteur]
BioComputing
Jacques, Philippe [Auteur]
Institut Charles Viollette (ICV) - EA 7394 [ICV]
Laboratoire de Procédés Biologiques, Génie Enzymatique et Microbien [ProBioGEM]
Titre de la revue :
Biotechnology Journal
Pagination :
1216-34
Éditeur :
Wiley-VCH Verlag
Date de publication :
2015-08-01
ISSN :
1860-6768
Mot(s)-clé(s) en anglais :
Abstract interpretation
Metabolic networks
Qualitative reasonning
Modeling language
Bacillus subtilis
Knockout prediction
Systems biology
Surfactin
Metabolic networks
Qualitative reasonning
Modeling language
Bacillus subtilis
Knockout prediction
Systems biology
Surfactin
Discipline(s) HAL :
Informatique [cs]/Bio-informatique [q-bio.QM]
Sciences du Vivant [q-bio]/Biotechnologies
Sciences du Vivant [q-bio]/Biotechnologies
Résumé en anglais : [en]
A Bacillus subtilis mutant strain overexpressing surfactin biosynthetic genes was previously constructed. In order to further increase the production of this biosurfactant, our hypothesis is that the surfactin precursors, ...
Lire la suite >A Bacillus subtilis mutant strain overexpressing surfactin biosynthetic genes was previously constructed. In order to further increase the production of this biosurfactant, our hypothesis is that the surfactin precursors, especially leucine, must be overproduced. We present a three step approach for leucine overproduction directed by methods from computational biology. Firstly, we develop a new algorithm for gene knockout prediction based on abstract interpretation, which applies to a recent modeling language for reaction networks with partial kinetic information. Secondly, we model the leucine metabolic pathway as a reaction network in this language, and apply the knockout prediction algorithm with the target of leucine overproduction. Out of the 21 reactions corresponding to potential gene knockouts, the prediction algorithm selects 12 reactions. Six knockouts were introduced in B. subtilis 168 derivatives strains to verify their effects on surfactin production. For all generated mutants, the specific surfactin production is increased from 1.6 to 20.9 fold during the exponential growth phase, depending on the medium composition. These results show the effectiveness of the knockout prediction approach based on formal models for metabolic reaction networks with partial kinetic information, and confirms our hypothesis that precursors supply is one of the main parameter to optimize surfactin overproduction.Lire moins >
Lire la suite >A Bacillus subtilis mutant strain overexpressing surfactin biosynthetic genes was previously constructed. In order to further increase the production of this biosurfactant, our hypothesis is that the surfactin precursors, especially leucine, must be overproduced. We present a three step approach for leucine overproduction directed by methods from computational biology. Firstly, we develop a new algorithm for gene knockout prediction based on abstract interpretation, which applies to a recent modeling language for reaction networks with partial kinetic information. Secondly, we model the leucine metabolic pathway as a reaction network in this language, and apply the knockout prediction algorithm with the target of leucine overproduction. Out of the 21 reactions corresponding to potential gene knockouts, the prediction algorithm selects 12 reactions. Six knockouts were introduced in B. subtilis 168 derivatives strains to verify their effects on surfactin production. For all generated mutants, the specific surfactin production is increased from 1.6 to 20.9 fold during the exponential growth phase, depending on the medium composition. These results show the effectiveness of the knockout prediction approach based on formal models for metabolic reaction networks with partial kinetic information, and confirms our hypothesis that precursors supply is one of the main parameter to optimize surfactin overproduction.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
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