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Computational Strategies for a System-Level Understanding of Metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational...

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Autores principales: Cazzaniga, Paolo, Damiani, Chiara, Besozzi, Daniela, Colombo, Riccardo, Nobile, Marco S., Gaglio, Daniela, Pescini, Dario, Molinari, Sara, Mauri, Giancarlo, Alberghina, Lilia, Vanoni, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279158/
https://www.ncbi.nlm.nih.gov/pubmed/25427076
http://dx.doi.org/10.3390/metabo4041034
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author Cazzaniga, Paolo
Damiani, Chiara
Besozzi, Daniela
Colombo, Riccardo
Nobile, Marco S.
Gaglio, Daniela
Pescini, Dario
Molinari, Sara
Mauri, Giancarlo
Alberghina, Lilia
Vanoni, Marco
author_facet Cazzaniga, Paolo
Damiani, Chiara
Besozzi, Daniela
Colombo, Riccardo
Nobile, Marco S.
Gaglio, Daniela
Pescini, Dario
Molinari, Sara
Mauri, Giancarlo
Alberghina, Lilia
Vanoni, Marco
author_sort Cazzaniga, Paolo
collection PubMed
description Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks.  In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.
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spelling pubmed-42791582014-12-30 Computational Strategies for a System-Level Understanding of Metabolism Cazzaniga, Paolo Damiani, Chiara Besozzi, Daniela Colombo, Riccardo Nobile, Marco S. Gaglio, Daniela Pescini, Dario Molinari, Sara Mauri, Giancarlo Alberghina, Lilia Vanoni, Marco Metabolites Review Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks.  In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided. MDPI 2014-11-24 /pmc/articles/PMC4279158/ /pubmed/25427076 http://dx.doi.org/10.3390/metabo4041034 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Cazzaniga, Paolo
Damiani, Chiara
Besozzi, Daniela
Colombo, Riccardo
Nobile, Marco S.
Gaglio, Daniela
Pescini, Dario
Molinari, Sara
Mauri, Giancarlo
Alberghina, Lilia
Vanoni, Marco
Computational Strategies for a System-Level Understanding of Metabolism
title Computational Strategies for a System-Level Understanding of Metabolism
title_full Computational Strategies for a System-Level Understanding of Metabolism
title_fullStr Computational Strategies for a System-Level Understanding of Metabolism
title_full_unstemmed Computational Strategies for a System-Level Understanding of Metabolism
title_short Computational Strategies for a System-Level Understanding of Metabolism
title_sort computational strategies for a system-level understanding of metabolism
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279158/
https://www.ncbi.nlm.nih.gov/pubmed/25427076
http://dx.doi.org/10.3390/metabo4041034
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