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Fine-Tuning Tomato Agronomic Properties by Computational Genome Redesign
Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obta...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3369923/ https://www.ncbi.nlm.nih.gov/pubmed/22685389 http://dx.doi.org/10.1371/journal.pcbi.1002528 |
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author | Carrera, Javier Fernández del Carmen, Asun Fernández-Muñoz, Rafael Rambla, Jose Luis Pons, Clara Jaramillo, Alfonso Elena, Santiago F. Granell, Antonio |
author_facet | Carrera, Javier Fernández del Carmen, Asun Fernández-Muñoz, Rafael Rambla, Jose Luis Pons, Clara Jaramillo, Alfonso Elena, Santiago F. Granell, Antonio |
author_sort | Carrera, Javier |
collection | PubMed |
description | Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence. Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties. The method was validated by the ability of the proposed genomes, engineered for modified desired agronomic traits, to recapitulate experimental correlations between associated metabolites. |
format | Online Article Text |
id | pubmed-3369923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-33699232012-06-08 Fine-Tuning Tomato Agronomic Properties by Computational Genome Redesign Carrera, Javier Fernández del Carmen, Asun Fernández-Muñoz, Rafael Rambla, Jose Luis Pons, Clara Jaramillo, Alfonso Elena, Santiago F. Granell, Antonio PLoS Comput Biol Research Article Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence. Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties. The method was validated by the ability of the proposed genomes, engineered for modified desired agronomic traits, to recapitulate experimental correlations between associated metabolites. Public Library of Science 2012-06-07 /pmc/articles/PMC3369923/ /pubmed/22685389 http://dx.doi.org/10.1371/journal.pcbi.1002528 Text en Carrera et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Carrera, Javier Fernández del Carmen, Asun Fernández-Muñoz, Rafael Rambla, Jose Luis Pons, Clara Jaramillo, Alfonso Elena, Santiago F. Granell, Antonio Fine-Tuning Tomato Agronomic Properties by Computational Genome Redesign |
title | Fine-Tuning Tomato Agronomic Properties by Computational Genome Redesign |
title_full | Fine-Tuning Tomato Agronomic Properties by Computational Genome Redesign |
title_fullStr | Fine-Tuning Tomato Agronomic Properties by Computational Genome Redesign |
title_full_unstemmed | Fine-Tuning Tomato Agronomic Properties by Computational Genome Redesign |
title_short | Fine-Tuning Tomato Agronomic Properties by Computational Genome Redesign |
title_sort | fine-tuning tomato agronomic properties by computational genome redesign |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3369923/ https://www.ncbi.nlm.nih.gov/pubmed/22685389 http://dx.doi.org/10.1371/journal.pcbi.1002528 |
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