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gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs
MOTIVATION: A widely applicable strategy to create cell factories is to knockout (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 deleter...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9272801/ https://www.ncbi.nlm.nih.gov/pubmed/35642935 http://dx.doi.org/10.1093/bioinformatics/btac376 |
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author | Legon, Laurence Corre, Christophe Bates, Declan G Mannan, Ahmad A |
author_facet | Legon, Laurence Corre, Christophe Bates, Declan G Mannan, Ahmad A |
author_sort | Legon, Laurence |
collection | PubMed |
description | MOTIVATION: A widely applicable strategy to create cell factories is to knockout (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 synthesize 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 maximize 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 AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9272801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92728012022-07-11 gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs Legon, Laurence Corre, Christophe Bates, Declan G Mannan, Ahmad A Bioinformatics Applications Notes MOTIVATION: A widely applicable strategy to create cell factories is to knockout (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 synthesize 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 maximize 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 AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-06-01 /pmc/articles/PMC9272801/ /pubmed/35642935 http://dx.doi.org/10.1093/bioinformatics/btac376 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Legon, Laurence Corre, Christophe Bates, Declan G Mannan, Ahmad A gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs |
title | gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs |
title_full | gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs |
title_fullStr | gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs |
title_full_unstemmed | gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs |
title_short | gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs |
title_sort | gcfront: a tool for determining a pareto front of growth-coupled cell factory designs |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9272801/ https://www.ncbi.nlm.nih.gov/pubmed/35642935 http://dx.doi.org/10.1093/bioinformatics/btac376 |
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