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Bioinformatics Advance Access published online on August 22, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl440
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 2, 2006
Revised August 9, 2006
Accepted August 10, 2006

Article

Systematic component selection for gene-network refinement

Nicole Radde 1 *, Jutta Gebert 1, and Christian V. Forst 2

1 Center for Applied Computer Science, University of Cologne, Weyertal 80, 50931 Cologne, Germany
2 Los Alamos National Laboratory, PO Box 1663, Mailstop M888, Los Alamos, NM 87545, USA

* To whom correspondence should be addressed.
Nicole Radde, E-mail: radde{at}zpr.uni-koeln.de


  Abstract

Motivation: A quantitative description of interactions between cell components is a major challenge in Computational Biology. As a method of choice, differential equations are used for this purpose, because they provide a detailed insight into the dynamic behavior of the system. In most cases, the number of time points of experimental time series is usually too small to estimate the parameters of a model of a whole gene regulatory network based on differential equations, such that one needs to focus on subnetworks consisting of only a few components. For most approaches, the set of components of the subsystem is given in advance and only the structure has to be estimated. However, the set of components that influence the system significantly are not always known in advance, making a method desirable that determines both, the components that are included into the model and the parameters.

Results: We have developed a method that uses gene expression data as well as interaction data between cell components to define a set of genes that we use for our modeling. In a subsequent step, we estimate the parameters of our model of piecewise linear differential equations and evaluate the results simulating the behavior of the system with our model.

We have applied our method to the DNA repair system of Mycobacterium tuberculosis. Our analysis predicts that the gene Rv2719c plays an important role in this system.

Associate Editor: Martin Bishop
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