Automated smoother for numerical decoupling of dynamic models

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/41996

Title: Automated smoother for numerical decoupling of dynamic models
Author: Vilela, Marco ; Borges, Carlos C. H. ; Vinga, Susana ; Vasconcelos, Ana Tereza R. ; Santos, Helena ; Voit, Eberhard O. ; Almeida, Jonas S.
Abstract: Background Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure. Results In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from in-vivo NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations. Conclusion The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental time series.
Description: © 2007 Vilela et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. DOI: 10.1186/1471-2105-8-305
Type: Article
URI: http://hdl.handle.net/1853/41996
ISSN: 1471-2105
Citation: Vilela, M., C. Borges, A. T. Vasconcelos, H. Santos, E. O. Voit. and J. S. Almeida, "Automated smoother for numerical decoupling of dynamic models,"BMC Bioinformatics 8:305, 2007.
Date: 2007-08
Contributor: Laboratório Nacional de Computação Científicas (Brazil)
Universidade Nova de Lisboa. Instituto de Tecnologia Química e Biológica
Instituto de Engenharia de Sistemas e Computadores. Investigação e Desenvolvimento
Georgia Institute of Technology. Dept. of Biomedical Engineering
Emory University. Dept. of Biomedical Engineering
University of Texas M.D. Anderson Cancer Center. Dept. of Bioinformatics and Computational Biology
Publisher: Georgia Institute of Technology
BioMed Central
Subject: Biochemical systems theory
Artificial neural networks
Numerical smoothing
Multivariate experimental time series

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