ArticleBMC systems biology2017
OptPipe - a pipeline for optimizing metabolic engineering targets.
Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes.Annual review of biomedical engineering · 2021Review
- Engineering intracellular malonyl-CoA availability in microbial hosts and its impact on polyketide and fatty acid synthesis.Applied microbiology and biotechnology · 2020Review
- MOMO - multi-objective metabolic mixed integer optimization: application to yeast strain engineering.BMC bioinformatics · 2020Article
- Characterizing and ranking computed metabolic engineering strategies.Bioinformatics (Oxford, England) · 2019Article
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Authors and funding
8 authors.
Funding
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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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