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Methods for integration of transcriptomic data in genome-scale metabolic models

Several computational methods have been developed that integrate transcriptomic data with genome-scale metabolic reconstructions to infer condition-specific system-wide intracellular metabolic flux distributions. In this mini-review, we describe each of these methods published to date with categoriz...

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Detalles Bibliográficos
Autores principales: Kim, Min Kyung, Lun, Desmond S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4212280/
https://www.ncbi.nlm.nih.gov/pubmed/25379144
http://dx.doi.org/10.1016/j.csbj.2014.08.009
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author Kim, Min Kyung
Lun, Desmond S.
author_facet Kim, Min Kyung
Lun, Desmond S.
author_sort Kim, Min Kyung
collection PubMed
description Several computational methods have been developed that integrate transcriptomic data with genome-scale metabolic reconstructions to infer condition-specific system-wide intracellular metabolic flux distributions. In this mini-review, we describe each of these methods published to date with categorizing them based on four different grouping criteria (requirement for multiple gene expression datasets as input, requirement for a threshold to define a gene's high and low expression, requirement for a priori assumption of an appropriate objective function, and validation of predicted fluxes directly against measured intracellular fluxes). Then, we recommend which group of methods would be more suitable from a practical perspective.
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spelling pubmed-42122802014-11-06 Methods for integration of transcriptomic data in genome-scale metabolic models Kim, Min Kyung Lun, Desmond S. Comput Struct Biotechnol J Mini Review Several computational methods have been developed that integrate transcriptomic data with genome-scale metabolic reconstructions to infer condition-specific system-wide intracellular metabolic flux distributions. In this mini-review, we describe each of these methods published to date with categorizing them based on four different grouping criteria (requirement for multiple gene expression datasets as input, requirement for a threshold to define a gene's high and low expression, requirement for a priori assumption of an appropriate objective function, and validation of predicted fluxes directly against measured intracellular fluxes). Then, we recommend which group of methods would be more suitable from a practical perspective. Research Network of Computational and Structural Biotechnology 2014-09-03 /pmc/articles/PMC4212280/ /pubmed/25379144 http://dx.doi.org/10.1016/j.csbj.2014.08.009 Text en © 2014 Kim and Lun. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology.
spellingShingle Mini Review
Kim, Min Kyung
Lun, Desmond S.
Methods for integration of transcriptomic data in genome-scale metabolic models
title Methods for integration of transcriptomic data in genome-scale metabolic models
title_full Methods for integration of transcriptomic data in genome-scale metabolic models
title_fullStr Methods for integration of transcriptomic data in genome-scale metabolic models
title_full_unstemmed Methods for integration of transcriptomic data in genome-scale metabolic models
title_short Methods for integration of transcriptomic data in genome-scale metabolic models
title_sort methods for integration of transcriptomic data in genome-scale metabolic models
topic Mini Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4212280/
https://www.ncbi.nlm.nih.gov/pubmed/25379144
http://dx.doi.org/10.1016/j.csbj.2014.08.009
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