Automated smoother for numerical decoupling of dynamic models

dc.contributor.author Vilela, Marco en_US
dc.contributor.author Borges, Carlos C. H. en_US
dc.contributor.author Vinga, Susana en_US
dc.contributor.author Vasconcelos, Ana Tereza R. en_US
dc.contributor.author Santos, Helena en_US
dc.contributor.author Voit, Eberhard O. en_US
dc.contributor.author Almeida, Jonas S. en_US
dc.contributor.corporatename Laboratório Nacional de Computação Científicas (Brazil) en_US
dc.contributor.corporatename Universidade Nova de Lisboa. Instituto de Tecnologia Química e Biológica en_US
dc.contributor.corporatename Instituto de Engenharia de Sistemas e Computadores. Investigação e Desenvolvimento en_US
dc.contributor.corporatename Georgia Institute of Technology. Dept. of Biomedical Engineering en_US
dc.contributor.corporatename Emory University. Dept. of Biomedical Engineering en_US
dc.contributor.corporatename University of Texas M.D. Anderson Cancer Center. Dept. of Bioinformatics and Computational Biology en_US
dc.date.accessioned 2011-11-11T21:07:32Z
dc.date.available 2011-11-11T21:07:32Z
dc.date.issued 2007-08
dc.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. en_US
dc.description DOI: 10.1186/1471-2105-8-305 en_US
dc.description.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. en_US
dc.identifier.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. en_US
dc.identifier.issn 1471-2105
dc.identifier.uri http://hdl.handle.net/1853/41996
dc.language.iso en_US en
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original BioMed Central en_US
dc.subject Biochemical systems theory en_US
dc.subject Artificial neural networks en_US
dc.subject Numerical smoothing en_US
dc.subject Multivariate experimental time series en_US
dc.title Automated smoother for numerical decoupling of dynamic models en_US
dc.type Text
dc.type.genre Article
dspace.entity.type Publication
local.contributor.author Voit, Eberhard O.
local.contributor.corporatename Wallace H. Coulter Department of Biomedical Engineering
local.contributor.corporatename College of Engineering
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