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Inferring cell cycle feedback regulation from gene expression data.

Articolo
Data di Pubblicazione:
2011
Abstract:
Feedback control is an important regulatory process in biological systems, which confers robustness against external and internal disturbances. Genes involved in feedback structures are therefore likely to have a major role in regulating cellular processes. Here we rely on a dynamic Bayesian network approach to identify feedback loops in cell cycle regulation. We analyzed the transcriptional profile of the cell cycle in HeLa cancer cells and identified a feedback loop structure composed of 10 genes. In silico analyses showed that these genes hold important roles in system's dynamics. The results of published experimental assays confirmed the central role of 8 of the identified feedback loop genes in cell cycle regulation. In conclusion, we provide a novel approach to identify critical genes for the dynamics of biological processes. This may lead to the identification of therapeutic targets in diseases that involve perturbations of these dynamics.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
gene expression data; Mathematical models of biological systems; biomedical informatics
Elenco autori:
Ferrazzi, Fulvia; Engel, Fb; Wu, E; Moseman, Ap; Kohane, Isaak; Bellazzi, Riccardo; Ramoni, Marco
Autori di Ateneo:
BELLAZZI RICCARDO
FERRAZZI FULVIA
Link alla scheda completa:
https://iris.unipv.it/handle/11571/223665
Pubblicato in:
JOURNAL OF BIOMEDICAL INFORMATICS
Journal
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