OptPipe - a pipeline for optimizing metabolic engineering targets

Abstract : Background: We propose OptPipe-a Pipeline for Optimizing Metabolic Engineering Targets, based on a consensus approach. The method generates consensus hypotheses for metabolic engineering applications by combining several optimization solutions obtained from distinct algorithms. The solutions are ranked according to several objectives, such as biomass and target production, by using the rank product tests corrected for multiple comparisons. Results: OptPipe was applied in a genome-scale model of Corynebacterium glutamicum for maximizing malonyl-CoA, which is a valuable precursor for many phenolic compounds. In vivo experimental validation confirmed increased malonyl-CoA level in case of sdhCAB deletion, as predicted in silico. Conclusions: A method was developed to combine the optimization solutions provided by common knockout prediction procedures and rank the suggested mutants according to the expected growth rate, production and a new adaptability measure. The implementation of the pipeline along with the complete documentation is freely available at https://github.com/AndrasHartmann/OptPipe.
Type de document :
Article dans une revue
BMC Systems Biology, BioMed Central, 2017, 11, pp.1-9. 〈10.1186/s12918-017-0515-0〉
Liste complète des métadonnées

Littérature citée [34 références]  Voir  Masquer  Télécharger

Contributeur : Marie-France Sagot <>
Soumis le : mercredi 27 décembre 2017 - 16:48:49
Dernière modification le : jeudi 21 mars 2019 - 14:51:22


Publication financée par une institution




András Hartmann, Ana Vila-Santa, Nicolai Kallscheuer, Michael Vogt, Alice Julien-Laferrière, et al.. OptPipe - a pipeline for optimizing metabolic engineering targets. BMC Systems Biology, BioMed Central, 2017, 11, pp.1-9. 〈10.1186/s12918-017-0515-0〉. 〈hal-01672905〉



Consultations de la notice


Téléchargements de fichiers