Biochemical networks with uncertain parameters.
Syst Biol (Stevenage)
; 152(3): 97-107, 2005 Sep.
Article
em En
| MEDLINE
| ID: mdl-16986274
ABSTRACT
The modelling of biochemical networks becomes delicate if kinetic parameters are varying, uncertain or unknown. Facing this situation, we quantify uncertain knowledge or beliefs about parameters by probability distributions. We show how parameter distributions can be used to infer probabilistic statements about dynamic network properties, such as steady-state fluxes and concentrations, signal characteristics or control coefficients. The parameter distributions can also serve as priors in Bayesian statistical analysis. We propose a graphical scheme, the 'dependence graph', to bring out known dependencies between parameters, for instance, due to the equilibrium constants. If a parameter distribution is narrow, the resulting distribution of the variables can be computed by expanding them around a set of mean parameter values. We compute the distributions of concentrations, fluxes and probabilities for qualitative variables such as flux directions. The probabilistic framework allows the study of metabolic correlations, and it provides simple measures of variability and stochastic sensitivity. It also shows clearly how the variability of biological systems is related to the metabolic response coefficients.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Bioquímica
/
Transdução de Sinais
/
Fenômenos Fisiológicos Celulares
/
Modelos Estatísticos
/
Proteoma
/
Modelos Biológicos
Tipo de estudo:
Prognostic_studies
/
Qualitative_research
/
Risk_factors_studies
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2005
Tipo de documento:
Article