Processamento de perfis metabólicos / Metabolic profiles processing
AUTOR(ES)
Marco Antônio Vilela
DATA DE PUBLICAÇÃO
2007
RESUMO
During the past 30-years, Biochemical System Theory (BST) has been provided a concrete foundation for the study of the dynamic biological systems, e.g. S-systems models for reverse engineering of metabolic networks (Savageau, 1969; Savageau, 1970; Voit, 2000). One of the remarkable characteristics of these models is its parameters not only quantify the interactions between the components of the network, but also elucidate the networks topology. Automatic procedures for S-system parameterization from biological time series have been developed by many researches, where they assume a noise-free time series and a true estimated first derivative in their methodologies (Chou, et al., 2006; Kikuchi, et al., 2003). Nevertheless, this noise-free data is not a realistic scenario of the real biological experimental world. Methods as artificial neural network (ANN), Support Vectors Machines (SVM) and Saviztsky-Golay filter were proposed to overcome the denoising time series problem with the advantage of a closed form output which allowed determining the first derivative symbolically (Almeida and Voit, 2003; Borges, et al., 2006; Borges, et al., 2004; Voit and Almeida, 2004). However, these solutions showed some problematic artifacts in its first derivative even when they are not visually apparent in the smoothed data, leaving a gap on the issue of a fully automatic method for S-system parameterization from experimental data. The algorithm presented in this work is a proposal to fill this gap up providing an unbiased robust tool for signal extraction and first derivative estimation from noisy time series.
ASSUNTO(S)
metabolismo metabolismo e bioenergetica metabolism análise de séries temporais time-series analysis processamento de perfis metabólicos suavização e extração da derivada temporal
