Modeling Leucine’s Metabolic Pathway and Knockout Prediction Improving the Production of Surfactin, a Biosurfactant from Bacillus Subtilis
2015
Coutte, François | Niehren, Joachim | Dhali, Debarun | John, Mathias | Versari, Cristian | Jacques, Philippe | Institut Charles Viollette (ICV) - EA 7394 (ICV) ; Université d'Artois (UA)-Institut National de la Recherche Agronomique (INRA)-Université du Littoral Côte d'Opale (ULCO)-Institut Supérieur d'Agriculture-Université de Lille | Laboratoire de Procédés Biologiques, Génie Enzymatique et Microbien (ProBioGEM) ; Université de Lille, Sciences et Technologies-École polytechnique universitaire de Lille (Polytech Lille) | BioComputing ; Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL) ; Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS) | Linking Dynamic Data (LINKS) ; Centre Inria de l'Université de Lille ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL) ; Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS) | ANR-10-BINF-0006,Iceberg,Des modèles de population aux populations de modèles: observation, modélisation et contrôle de l'expression génique au niveau de la cellule unique(2010)
International audience
Show more [+] Less [-]English. 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.
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