Parameter optimization in S-system models

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/41998

Title: Parameter optimization in S-system models
Author: Vilela, Marco ; Chou, I-Chun ; Vinga, Susana ; Vasconcelos, Ana Tereza R. ; Voit, Eberhard O. ; Almeida, Jonas S.
Abstract: Background: The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential Ssystem equations, which results in a set of algebraic equations. Results: A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion: A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.
Description: © 2008 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: 10.1186/1752-0509-2-35
Type: Article
URI: http://hdl.handle.net/1853/41998
ISSN: 1752-0509
Citation: Vilela, M., I-C.Chou, S. Vinga, A.T.R Vasconcelos, E.O. Voit, and J.S. Almeida, "Parameter optimization in S-system models,"BMC Systems Biol. 16; 2:35, 2008.
Date: 2008-04
Contributor: University of Texas M.D. Anderson Cancer Center. Dept. of Bioinformatics and Computational Biology
Georgia Institute of Technology. Dept. of Biomedical Engineering
Emory University. Dept. of Biomedical Engineering
Instituto de Engenharia de Sistemas e Computadores. Investigação e Desenvolvimento
Laboratório Nacional de Computação Científicas (Brazil)
Universidade Nova de Lisboa. Instituto de Tecnologia Química e Biológica
Publisher: Georgia Institute of Technology
BioMed Central
Subject: Biological networks
Time series data
Systems biology
Parameter identification

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