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Bioinformatics Advance Access published online on January 6, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp004
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A divide–and–conquer approach to analyze underdetermined biochemical models

Oliver Kotte 1,2 and Matthias Heinemann 1,*

1Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland.
2Institute of Process Engineering, ETH Zurich, 8092 Zurich, Switzerland.

*To whom correspondence should be addressed. Dr. Matthias Heinemann, E-mail: heinemann{at}imsb.biol.ethz.ch


  Abstract

Motivation: To obtain meaningful predictions from dynamic computational models, their uncertain parameter values need to be estimated from experimental data. Due to the usually large number of parameters compared to the available measurement data, these estimation problems are often underdetermined meaning that the solution is a multidimensional space. In this case, the challenge is yet to obtain a sound system understanding despite non–identifiable parameter values, e.g. through identifying those parameters that most sensitively determine the model's behavior.

Results: Here, we present the so–called divide-and-conquer approach — a strategy to analyze underdetermined biochemical models. The approach draws on steady state -omics measurement data and exploits a decomposition of the global estimation problem into independent subproblems. The solutions to these subproblems are joined to the complete space of global optima, which can be easily analyzed. We derive the conditions at which the decomposition occurs, outline strategies to fulfill these conditions, and — using an example model — illustrate how the approach uncovers the most important parameters and suggests targeted experiments without knowing the exact parameter values.

Contact: heinemann{at}imsb.biol.ethz.ch

Associate Editor: Prof. John Quackenbush

Received on September 25, 2008; revised on December 12, 2008; accepted on December 30, 2008

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