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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2014
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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. |
format | Online Article Text |
id | pubmed-4279158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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