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Strategies for the multiplex mapping of genes to traits
Rewiring and optimization of metabolic networks to enable the production of commercially valuable chemicals is a central goal of metabolic engineering. This prospect is challenged by the complexity of metabolic networks, lack of complete knowledge of gene function(s), and the vast combinatorial geno...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3842685/ https://www.ncbi.nlm.nih.gov/pubmed/24171944 http://dx.doi.org/10.1186/1475-2859-12-99 |
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author | Garst, Andrew Lynch, Michael Evans, Ron Gill, Ryan T |
author_facet | Garst, Andrew Lynch, Michael Evans, Ron Gill, Ryan T |
author_sort | Garst, Andrew |
collection | PubMed |
description | Rewiring and optimization of metabolic networks to enable the production of commercially valuable chemicals is a central goal of metabolic engineering. This prospect is challenged by the complexity of metabolic networks, lack of complete knowledge of gene function(s), and the vast combinatorial genotype space that is available for exploration and optimization. Various approaches have thus been developed to aid in the efficient identification of genes that contribute to a variety of different phenotypes, allowing more rapid design and engineering of traits desired for industrial applications. This review will highlight recent technologies that have enhanced capabilities to map genotype-phenotype relationships on a genome wide scale and emphasize how such approaches enable more efficient design and engineering of complex phenotypes. |
format | Online Article Text |
id | pubmed-3842685 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-38426852013-11-29 Strategies for the multiplex mapping of genes to traits Garst, Andrew Lynch, Michael Evans, Ron Gill, Ryan T Microb Cell Fact Review Rewiring and optimization of metabolic networks to enable the production of commercially valuable chemicals is a central goal of metabolic engineering. This prospect is challenged by the complexity of metabolic networks, lack of complete knowledge of gene function(s), and the vast combinatorial genotype space that is available for exploration and optimization. Various approaches have thus been developed to aid in the efficient identification of genes that contribute to a variety of different phenotypes, allowing more rapid design and engineering of traits desired for industrial applications. This review will highlight recent technologies that have enhanced capabilities to map genotype-phenotype relationships on a genome wide scale and emphasize how such approaches enable more efficient design and engineering of complex phenotypes. BioMed Central 2013-10-30 /pmc/articles/PMC3842685/ /pubmed/24171944 http://dx.doi.org/10.1186/1475-2859-12-99 Text en Copyright © 2013 Garst et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Garst, Andrew Lynch, Michael Evans, Ron Gill, Ryan T Strategies for the multiplex mapping of genes to traits |
title | Strategies for the multiplex mapping of genes to traits |
title_full | Strategies for the multiplex mapping of genes to traits |
title_fullStr | Strategies for the multiplex mapping of genes to traits |
title_full_unstemmed | Strategies for the multiplex mapping of genes to traits |
title_short | Strategies for the multiplex mapping of genes to traits |
title_sort | strategies for the multiplex mapping of genes to traits |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3842685/ https://www.ncbi.nlm.nih.gov/pubmed/24171944 http://dx.doi.org/10.1186/1475-2859-12-99 |
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