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RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations
Predicting cellular responses to perturbations is an important task in systems biology. We report a new approach, RELATCH, which uses flux and gene expression data from a reference state to predict metabolic responses in a genetically or environmentally perturbed state. Using the concept of relative...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3506949/ https://www.ncbi.nlm.nih.gov/pubmed/23013597 http://dx.doi.org/10.1186/gb-2012-13-9-r78 |
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author | Kim, Joonhoon Reed, Jennifer L |
author_facet | Kim, Joonhoon Reed, Jennifer L |
author_sort | Kim, Joonhoon |
collection | PubMed |
description | Predicting cellular responses to perturbations is an important task in systems biology. We report a new approach, RELATCH, which uses flux and gene expression data from a reference state to predict metabolic responses in a genetically or environmentally perturbed state. Using the concept of relative optimality, which considers relative flux changes from a reference state, we hypothesize a relative metabolic flux pattern is maintained from one state to another, and that cells adapt to perturbations using metabolic and regulatory reprogramming to preserve this relative flux pattern. This constraint-based approach will have broad utility where predictions of metabolic responses are needed. |
format | Online Article Text |
id | pubmed-3506949 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35069492012-11-29 RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations Kim, Joonhoon Reed, Jennifer L Genome Biol Method Predicting cellular responses to perturbations is an important task in systems biology. We report a new approach, RELATCH, which uses flux and gene expression data from a reference state to predict metabolic responses in a genetically or environmentally perturbed state. Using the concept of relative optimality, which considers relative flux changes from a reference state, we hypothesize a relative metabolic flux pattern is maintained from one state to another, and that cells adapt to perturbations using metabolic and regulatory reprogramming to preserve this relative flux pattern. This constraint-based approach will have broad utility where predictions of metabolic responses are needed. BioMed Central 2012 2012-09-26 /pmc/articles/PMC3506949/ /pubmed/23013597 http://dx.doi.org/10.1186/gb-2012-13-9-r78 Text en Copyright ©2012 Kim and Reed; 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 | Method Kim, Joonhoon Reed, Jennifer L RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations |
title | RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations |
title_full | RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations |
title_fullStr | RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations |
title_full_unstemmed | RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations |
title_short | RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations |
title_sort | relatch: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3506949/ https://www.ncbi.nlm.nih.gov/pubmed/23013597 http://dx.doi.org/10.1186/gb-2012-13-9-r78 |
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