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

Cazzaniga, P., Damiani, C., Besozzi, D., Colombo, R., Nobile, M., Gaglio, D., et al. (2014). Computational Strategies for a System-Level Understanding of Metabolism. METABOLITES, 4(4), 1034-1087 [10.3390/metabo4041034].

Computational Strategies for a System-Level Understanding of Metabolism

DAMIANI, CHIARA;BESOZZI, DANIELA;COLOMBO, RICCARDO;NOBILE, MARCO SALVATORE;GAGLIO, DANIELA;PESCINI, DARIO;MAURI, GIANCARLO;ALBERGHINA, LILIA;VANONI, MARCO ERCOLE
Ultimo
2014

Abstract

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
No
Articolo in rivista - Articolo scientifico
Scientifica
metabolism; metabolome; modeling; systems biology; genome-wide model; constraint-based model; core model; mechanistic model; ensemble modeling; parameter estimation; reverse engineering; flux balance analysis; network analysis; sensitivity analysis; control theory
English
1034
1087
54
Cazzaniga, P., Damiani, C., Besozzi, D., Colombo, R., Nobile, M., Gaglio, D., et al. (2014). Computational Strategies for a System-Level Understanding of Metabolism. METABOLITES, 4(4), 1034-1087 [10.3390/metabo4041034].
Cazzaniga, P; Damiani, C; Besozzi, D; Colombo, R; Nobile, M; Gaglio, D; Pescini, D; Molinari, S; Mauri, G; Alberghina, L; Vanoni, M
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/62715
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