OptPipe - a pipeline for optimizing metabolic engineering targets |
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Authors: | András Hartmann Ana Vila-Santa Nicolai Kallscheuer Michael Vogt Alice Julien-Laferrière Marie-France Sagot Jan Marienhagen Susana Vinga |
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Institution: | 1.IDMEC, Instituto Superior Técnico,Universidade de Lisboa,Lisbon,Portugal;2.Institute of Bio- and Geosciences,IBG-1: Biotechnology Forschungszentrum Jülich GmbH,Jülich,Germany;3.EPI ERABLE,Inria Grenoble,Rh?ne-Alpes,France;4.Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS,UMR5558, Laboratoire de Biométrie et Biologie Evolutive,Villeurbanne,France |
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Abstract: | BackgroundWe 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.ResultsOptPipe 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 ΔsdhCAB deletion, as predicted in silico.ConclusionsA 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. |
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