Priming nonlinear searches for pathway identification
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Priming nonlinear searches for pathway identification Siren R Veflingstad1,2, Jonas Almeida3 and Eberhard O Voit*3,4 Address: 1Department of Chemistry, Biotechnology and Food Science, Agricultural University of Norway, N-1432 Ås, Norway, 2Center for Integrative Genetics (Cigene), Agricultural University of Norway, N-1432 Ås, Norway, 3Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, 303K Cannon Place, 135 Cannon Street, Charleston, SC 29425, USA and 4Department of Biochemistry and Molecular Biology, Medical University of South Carolina, 303K Cannon Place, 171 Ashley Avenue, Charleston, SC 29425, USA Email: Siren R Veflingstad - [email protected]; Jonas Almeida - [email protected]; Eberhard O Voit* - [email protected] * Corresponding author
Published: 14 September 2004 Theoretical Biology and Medical Modelling 2004, 1:8
doi:10.1186/1742-4682-1-8
Received: 12 August 2004 Accepted: 14 September 2004
This article is available from: http://www.tbiomed.com/content/1/1/8 © 2004 Veflingstad 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.
Abstract Background: Dense time series of metabolite concentrations or of the expression patterns of proteins may be available in the near future as a result of the rapid development of novel, highthroughput experimental techniques. Such time series implicitly contain valuable information about the connectivity and regulatory structure of the underlying metabolic or proteomic networks. The extraction of this information is a challenging task because it usually requires nonlinear estimation methods that involve iterative search algorithms. Priming these algorithms with high-quality initial guesses can greatly accelerate the search process. In this article, we propose to obtain such guesses by preprocessing the temporal profile data and fitting them preliminarily by multivariate linear regression. Results: The results of a small-scale analysis indicate that the regression coefficients reflect the connectivity of the network quite well. Using the mathematical modeling framework of Biochemical Systems Theory (BST), we also show that the regression coefficients may be translated into constraints on the parameter values of the nonlinear BST model, thereby reducing the parameter search space considerably. Conclusion: The proposed method provides a good approach for obtaining a preliminary network structure from dense time series. This will be more valuable as the systems become larger, because preprocessing and effective priming can significantly limit the search space of parameters defining the network connectivity, thereby facilitating the nonlinear estimation task.
Introduction The rapid development of experimental tools like nuclear
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