Title:
Priming nonlinear searches for pathway identification
Priming nonlinear searches for pathway identification
Author(s)
Veflingstad, Siren R.
Almeida, Jonas S.
Voit, Eberhard O.
Almeida, Jonas S.
Voit, Eberhard O.
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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.
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Date Issued
2004-09-14
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Article