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Integration of enzyme activities into metabolic flux distributions by elementary mode analysis
BACKGROUND: In systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to pr...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1973080/ https://www.ncbi.nlm.nih.gov/pubmed/17640350 http://dx.doi.org/10.1186/1752-0509-1-31 |
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author | Kurata, Hiroyuki Zhao, Quanyu Okuda, Ryuichi Shimizu, Kazuyuki |
author_facet | Kurata, Hiroyuki Zhao, Quanyu Okuda, Ryuichi Shimizu, Kazuyuki |
author_sort | Kurata, Hiroyuki |
collection | PubMed |
description | BACKGROUND: In systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model. RESULTS: To link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF. CONCLUSION: The ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of E. coli and B. subtilis. |
format | Text |
id | pubmed-1973080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-19730802007-09-08 Integration of enzyme activities into metabolic flux distributions by elementary mode analysis Kurata, Hiroyuki Zhao, Quanyu Okuda, Ryuichi Shimizu, Kazuyuki BMC Syst Biol Methodology Article BACKGROUND: In systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model. RESULTS: To link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF. CONCLUSION: The ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of E. coli and B. subtilis. BioMed Central 2007-07-18 /pmc/articles/PMC1973080/ /pubmed/17640350 http://dx.doi.org/10.1186/1752-0509-1-31 Text en Copyright © 2007 Kurata 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 | Methodology Article Kurata, Hiroyuki Zhao, Quanyu Okuda, Ryuichi Shimizu, Kazuyuki Integration of enzyme activities into metabolic flux distributions by elementary mode analysis |
title | Integration of enzyme activities into metabolic flux distributions by elementary mode analysis |
title_full | Integration of enzyme activities into metabolic flux distributions by elementary mode analysis |
title_fullStr | Integration of enzyme activities into metabolic flux distributions by elementary mode analysis |
title_full_unstemmed | Integration of enzyme activities into metabolic flux distributions by elementary mode analysis |
title_short | Integration of enzyme activities into metabolic flux distributions by elementary mode analysis |
title_sort | integration of enzyme activities into metabolic flux distributions by elementary mode analysis |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1973080/ https://www.ncbi.nlm.nih.gov/pubmed/17640350 http://dx.doi.org/10.1186/1752-0509-1-31 |
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