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Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models

Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficul...

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Autores principales: Yuan, Qianqian, Huang, Teng, Li, Peishun, Hao, Tong, Li, Feiran, Ma, Hongwu, Wang, Zhiwen, Zhao, Xueming, Chen, Tao, Goryanin, Igor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5234801/
https://www.ncbi.nlm.nih.gov/pubmed/28085902
http://dx.doi.org/10.1371/journal.pone.0169437
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author Yuan, Qianqian
Huang, Teng
Li, Peishun
Hao, Tong
Li, Feiran
Ma, Hongwu
Wang, Zhiwen
Zhao, Xueming
Chen, Tao
Goryanin, Igor
author_facet Yuan, Qianqian
Huang, Teng
Li, Peishun
Hao, Tong
Li, Feiran
Ma, Hongwu
Wang, Zhiwen
Zhao, Xueming
Chen, Tao
Goryanin, Igor
author_sort Yuan, Qianqian
collection PubMed
description Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design.
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spelling pubmed-52348012017-02-06 Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models Yuan, Qianqian Huang, Teng Li, Peishun Hao, Tong Li, Feiran Ma, Hongwu Wang, Zhiwen Zhao, Xueming Chen, Tao Goryanin, Igor PLoS One Research Article Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design. Public Library of Science 2017-01-13 /pmc/articles/PMC5234801/ /pubmed/28085902 http://dx.doi.org/10.1371/journal.pone.0169437 Text en © 2017 Yuan 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Yuan, Qianqian
Huang, Teng
Li, Peishun
Hao, Tong
Li, Feiran
Ma, Hongwu
Wang, Zhiwen
Zhao, Xueming
Chen, Tao
Goryanin, Igor
Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models
title Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models
title_full Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models
title_fullStr Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models
title_full_unstemmed Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models
title_short Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models
title_sort pathway-consensus approach to metabolic network reconstruction for pseudomonas putida kt2440 by systematic comparison of published models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5234801/
https://www.ncbi.nlm.nih.gov/pubmed/28085902
http://dx.doi.org/10.1371/journal.pone.0169437
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