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Metabolic Reconstruction and Modeling of Nitrogen Fixation in Rhizobium etli
Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which pro...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2007
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2000972/ https://www.ncbi.nlm.nih.gov/pubmed/17922569 http://dx.doi.org/10.1371/journal.pcbi.0030192 |
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author | Resendis-Antonio, Osbaldo Reed, Jennifer L Encarnación, Sergio Collado-Vides, Julio Palsson, Bernhard Ø |
author_facet | Resendis-Antonio, Osbaldo Reed, Jennifer L Encarnación, Sergio Collado-Vides, Julio Palsson, Bernhard Ø |
author_sort | Resendis-Antonio, Osbaldo |
collection | PubMed |
description | Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation. |
format | Text |
id | pubmed-2000972 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-20009722007-10-25 Metabolic Reconstruction and Modeling of Nitrogen Fixation in Rhizobium etli Resendis-Antonio, Osbaldo Reed, Jennifer L Encarnación, Sergio Collado-Vides, Julio Palsson, Bernhard Ø PLoS Comput Biol Research Article Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation. Public Library of Science 2007-10 2007-10-05 /pmc/articles/PMC2000972/ /pubmed/17922569 http://dx.doi.org/10.1371/journal.pcbi.0030192 Text en © 2007 Resendis-Antonio 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 Resendis-Antonio, Osbaldo Reed, Jennifer L Encarnación, Sergio Collado-Vides, Julio Palsson, Bernhard Ø Metabolic Reconstruction and Modeling of Nitrogen Fixation in Rhizobium etli |
title | Metabolic Reconstruction and Modeling of Nitrogen Fixation in Rhizobium etli
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title_full | Metabolic Reconstruction and Modeling of Nitrogen Fixation in Rhizobium etli
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title_fullStr | Metabolic Reconstruction and Modeling of Nitrogen Fixation in Rhizobium etli
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title_full_unstemmed | Metabolic Reconstruction and Modeling of Nitrogen Fixation in Rhizobium etli
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title_short | Metabolic Reconstruction and Modeling of Nitrogen Fixation in Rhizobium etli
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title_sort | metabolic reconstruction and modeling of nitrogen fixation in rhizobium etli |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2000972/ https://www.ncbi.nlm.nih.gov/pubmed/17922569 http://dx.doi.org/10.1371/journal.pcbi.0030192 |
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