title: Modeling leucine's metabolic pathway and knockout prediction improving the production of surfactin, a biosurfactant from Bacillus subtilis.
abstract: 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 parameters to optimize surfactin overproduction.
authors.affiliation: ProBioGEM team, Research Institute for Food and Biotechnology - Charles Viollette (EA7394), University of Lille, Villeneuve d'Ascq, France. francois.coutte@polytech-lille.fr.|University of Lille, Villeneuve d'Ascq, France. francois.coutte@polytech-lille.fr.
authors.forename: François
authors.initials: F
authors.lastname: Coutte
authors.affiliation: BioComputing team, CRIStAL Lab (CNRS UMR9189), University of Lille, Villeneuve d'Ascq, France.|Inria Lille, Villeneuve d'Ascq, France.
authors.forename: Joachim
authors.initials: J
authors.lastname: Niehren
authors.affiliation: ProBioGEM team, Research Institute for Food and Biotechnology - Charles Viollette (EA7394), University of Lille, Villeneuve d'Ascq, France.|University of Lille, Villeneuve d'Ascq, France.
authors.forename: Debarun
authors.initials: D
authors.lastname: Dhali
authors.affiliation: University of Lille, Villeneuve d'Ascq, France.|BioComputing team, CRIStAL Lab (CNRS UMR9189), University of Lille, Villeneuve d'Ascq, France.
authors.forename: Mathias
authors.initials: M
authors.lastname: John
authors.affiliation: University of Lille, Villeneuve d'Ascq, France.|BioComputing team, CRIStAL Lab (CNRS UMR9189), University of Lille, Villeneuve d'Ascq, France.
authors.forename: Cristian
authors.initials: C
authors.lastname: Versari
authors.affiliation: ProBioGEM team, Research Institute for Food and Biotechnology - Charles Viollette (EA7394), University of Lille, Villeneuve d'Ascq, France.|University of Lille, Villeneuve d'Ascq, France.
authors.forename: Philippe
authors.initials: P
authors.lastname: Jacques
mesh_terms: D001412:Bacillus subtilis / Q000235:genetics / Q000378:metabolism*; D055786:Gene Knockout Techniques; D007930:Leucine / Q000378:metabolism*; D055666:Lipopeptides / Q000378:metabolism*; D060847:Metabolic Engineering; D053858:Metabolic Networks and Pathways / Q000235:genetics; D008954:Models, Biological; D010456:Peptides, Cyclic / Q000378:metabolism*; D013501:Surface-Active Agents / Q000378:metabolism*
chemical_list: D055666:Lipopeptides; D010456:Peptides, Cyclic; D013501:Surface-Active Agents; C009365:surfactin peptide; D007930:Leucine
keywords: Abstract interpretation; Bacillus subtilis; Knockout prediction; Modeling language; Surfactin