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MEWpy: a computational strain optimization workbench in Python
SUMMARY: Metabolic Engineering aims to favour the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical appro...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8388025/ https://www.ncbi.nlm.nih.gov/pubmed/33459757 http://dx.doi.org/10.1093/bioinformatics/btab013 |
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author | Pereira, Vítor Cruz, Fernando Rocha, Miguel |
author_facet | Pereira, Vítor Cruz, Fernando Rocha, Miguel |
author_sort | Pereira, Vítor |
collection | PubMed |
description | SUMMARY: Metabolic Engineering aims to favour the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, which covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms. AVAILABILITY AND IMPLEMENTATION: MEWpy can be installed from PyPi (pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license. |
format | Online Article Text |
id | pubmed-8388025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-83880252021-08-26 MEWpy: a computational strain optimization workbench in Python Pereira, Vítor Cruz, Fernando Rocha, Miguel Bioinformatics Applications Notes SUMMARY: Metabolic Engineering aims to favour the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, which covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms. AVAILABILITY AND IMPLEMENTATION: MEWpy can be installed from PyPi (pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license. Oxford University Press 2021-01-18 /pmc/articles/PMC8388025/ /pubmed/33459757 http://dx.doi.org/10.1093/bioinformatics/btab013 Text en © The Author(s) 2021. 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 (http://creativecommons.org/licenses/by/4.0/ (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 Pereira, Vítor Cruz, Fernando Rocha, Miguel MEWpy: a computational strain optimization workbench in Python |
title | MEWpy: a computational strain optimization workbench in Python |
title_full | MEWpy: a computational strain optimization workbench in Python |
title_fullStr | MEWpy: a computational strain optimization workbench in Python |
title_full_unstemmed | MEWpy: a computational strain optimization workbench in Python |
title_short | MEWpy: a computational strain optimization workbench in Python |
title_sort | mewpy: a computational strain optimization workbench in python |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8388025/ https://www.ncbi.nlm.nih.gov/pubmed/33459757 http://dx.doi.org/10.1093/bioinformatics/btab013 |
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