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  GeneReg: A constraint-based approach for design of feasible metabolic engineering strategies at the gene level

Razaghi-Moghadam, Z., & Nikoloski, Z. (2020). GeneReg: A constraint-based approach for design of feasible metabolic engineering strategies at the gene level. Bioinformatics. doi:10.1093/bioinformatics/btaa996.

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Razaghi-Moghadam, Z.1, Author              
Nikoloski, Z.1, Author              
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1Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              

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 Abstract: Large-scale metabolic models are widely used to design metabolic engineering strategies for diverse biotechnological applications. However, the existing computational approaches focus on alteration of reaction fluxes and often neglect the manipulations of gene expression to implement these strategies.Here we find that the association of genes with multiple reactions leads to infeasibility of engineering strategies at the flux level, since they require contradicting manipulations of gene expression. Moreover, we identify that all of the existing approaches to design gene knock-out strategies do not ensure that the resulting design may also require other gene alterations, such as up- or down-regulations, to match the desired flux distribution. To address these issues, we propose a constraint-based approach, termed GeneReg, that facilitates the design of feasible metabolic engineering strategies at the gene level and that is readily applicable to large-scale metabolic networks. We show that GeneReg can identify feasible strategies to overproduce ethanol in Escherichia coli and lactate in Saccharomyces cerevisiae, but overproduction of the TCA cycle intermediates is not feasible in five organisms used as cell factories under default growth conditions. Therefore, GeneReg points at the need to couple gene regulation and metabolism to design rational metabolic engineering strategies.https://github.com/MonaRazaghi/GeneReg

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Language(s): eng - English
 Dates: 2020-11
 Publication Status: Published in print
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 Identifiers: DOI: 10.1093/bioinformatics/btaa996
BibTex Citekey: 10.1093/bioinformatics/btaa996
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991