Statistical analysis of global connectivity and activity distributions in cellular networks.
J Comput Biol
; 17(7): 869-78, 2010 Jul.
Article
em En
| MEDLINE
| ID: mdl-20632868
ABSTRACT
Various molecular interaction networks have been claimed to follow power-law decay for their global connectivity distribution. It has been proposed that there may be underlying generative models that explain this heavy-tailed behavior by self-reinforcement processes such as classical or hierarchical scale-free network models. Here, we analyze a comprehensive data set of protein-protein and transcriptional regulatory interaction networks in yeast, an Escherichia coli metabolic network, and gene activity profiles for different metabolic states in both organisms. We show that in all cases the networks have a heavy-tailed distribution, but most of them present significant differences from a power-law model according to a stringent statistical test. Those few data sets that have a statistically significant fit with a power-law model follow other distributions equally well. Thus, while our analysis supports that both global connectivity interaction networks and activity distributions are heavy-tailed, they are not generally described by any specific distribution model, leaving space for further inferences on generative models. Supplementary Material is available online at www.liebertonline.com.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
3_ND
Problema de saúde:
3_neglected_diseases
/
3_zoonosis
Assunto principal:
Saccharomyces cerevisiae
/
Escherichia coli
/
Redes e Vias Metabólicas
/
Redes Reguladoras de Genes
Tipo de estudo:
Risk_factors_studies
Idioma:
En
Revista:
J Comput Biol
Assunto da revista:
BIOLOGIA MOLECULAR
/
INFORMATICA MEDICA
Ano de publicação:
2010
Tipo de documento:
Article
País de afiliação:
Espanha