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gcFront : a tool for determining a Pareto front of growth-coupled cell factory designs
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Legon, Laurence, Corre, Christophe, Bates, Declan G. and Mannan, Ahmad A. (2022) gcFront : a tool for determining a Pareto front of growth-coupled cell factory designs. Bioinformatics, 38 (14). pp. 3657-3659. doi:10.1093/bioinformatics/btac376 ISSN 1460-2059.
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Official URL: https://doi.org/10.1093/bioinformatics/btac376
Abstract
Motivation A widely applicable strategy to create cell factories is to knock out (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesise at high rates. Genome-scale metabolic models can be used to explore and identify KOs that growth-couple synthesis, but these are rare in an immense design space, making the search difficult and slow. Results To address this multi-objective optimization problem, we developed a software tool named gcFront - using a genetic algorithm it explores KOs that maximise cell growth, product synthesis, and coupling strength. Moreover, our measure of coupling strength facilitates the search so that gcFront not only finds a growth coupled design in minutes but also outputs many alternative Pareto optimal designs from a single run - granting users flexibility in selecting designs to take to the lab. Availability gcFront, with documentation and a workable tutorial, is freely available at GitHub: https://github.com/lLegon/gcFront and archived at Zenodo, DOI: 10.5281/zenodo.5557755 (Legon et al., 2022). Supplementary information Supplementary data are available at Bioinformatics online.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QH Natural history | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) |
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| SWORD Depositor: | Library Publications Router | |||||||||
| Library of Congress Subject Headings (LCSH): | Cell metabolism , Biological control systems, Cells -- Growth -- Regulation, Bioinformatics | |||||||||
| Journal or Publication Title: | Bioinformatics | |||||||||
| Publisher: | Oxford University Press (OUP) | |||||||||
| ISSN: | 1460-2059 | |||||||||
| Official Date: | July 2022 | |||||||||
| Dates: |
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| Volume: | 38 | |||||||||
| Number: | 14 | |||||||||
| Page Range: | pp. 3657-3659 | |||||||||
| DOI: | 10.1093/bioinformatics/btac376 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 18 August 2022 | |||||||||
| Date of first compliant Open Access: | 18 August 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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