GeneReg
- Motivation 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. Results 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 knockout strategies do not ensure that the resulting design may also require other gene alterations, such as up- or downregulations, 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 identifyMotivation 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. Results 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 knockout strategies do not ensure that the resulting design may also require other gene alterations, such as up- or downregulations, 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.…
Author details: | Zahra Razaghi-MoghadamORCiD, Zoran NikoloskiORCiDGND |
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DOI: | https://doi.org/10.1093/bioinformatics/btaa996 |
ISSN: | 1367-4803 |
ISSN: | 1460-2059 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/33245091 |
Title of parent work (English): | Bioinformatics |
Subtitle (English): | a constraint-based approach for design of feasible metabolic engineering strategies at the gene level |
Publisher: | Oxford Univ. Press |
Place of publishing: | Oxford |
Publication type: | Article |
Language: | English |
Date of first publication: | 2020/11/27 |
Publication year: | 2020 |
Release date: | 2023/03/24 |
Volume: | 37 |
Issue: | 12 |
Number of pages: | 7 |
First page: | 1717 |
Last Page: | 1723 |
Funding institution: | German Federal Ministry of Science and Education [031B0358B] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
Peer review: | Referiert |
Publishing method: | Open Access / Hybrid Open-Access |
License (German): | CC-BY-NC - Namensnennung, nicht kommerziell 4.0 International |