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Computational Strategies for a System-Level Understanding of Metabolism
- Source :
- Metabolites 4 (2014): 1034–1087. doi:10.3390/metabo4041034, info:cnr-pdr/source/autori:P. Cazzaniga, C. Damiani, D. Besozzi, R. Colombo, M.S. Nobile, D. Gaglio, D. Pescini, S. Molinari, G. Mauri, L. Alberghina, M, Vanoni/titolo:Computational strategies for a system-level understanding of metabolism/doi:10.3390%2Fmetabo4041034/rivista:Metabolites/anno:2014/pagina_da:1034/pagina_a:1087/intervallo_pagine:1034–1087/volume:4, Metabolites, Metabolites, Vol 4, Iss 4, Pp 1034-1087 (2014)
- Publication Year :
- 2014
- Publisher :
- M D P I AG, 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.
- Subjects :
- Reverse engineering
Computer science
Endocrinology, Diabetes and Metabolism
Systems biology
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
lcsh:QR1-502
Inference
Review
computer.software_genre
Biochemistry
lcsh:Microbiology
network analysi
Molecular Biology
Settore INF/01 - Informatica
Scale (chemistry)
INF/01 - INFORMATICA
sensitivity analysi
Data science
BIO/10 - BIOCHIMICA
flux balance analysi
System dynamics
Flux balance analysis
Identification (information)
Cell metabolism
ComputingMethodologies_PATTERNRECOGNITION
Biochemical engineering
computer
metabolism
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Metabolites 4 (2014): 1034–1087. doi:10.3390/metabo4041034, info:cnr-pdr/source/autori:P. Cazzaniga, C. Damiani, D. Besozzi, R. Colombo, M.S. Nobile, D. Gaglio, D. Pescini, S. Molinari, G. Mauri, L. Alberghina, M, Vanoni/titolo:Computational strategies for a system-level understanding of metabolism/doi:10.3390%2Fmetabo4041034/rivista:Metabolites/anno:2014/pagina_da:1034/pagina_a:1087/intervallo_pagine:1034–1087/volume:4, Metabolites, Metabolites, Vol 4, Iss 4, Pp 1034-1087 (2014)
- Accession number :
- edsair.doi.dedup.....1b36707959792924d3943c14510a80d0
- Full Text :
- https://doi.org/10.3390/metabo4041034