Bioinformatics Vol. 19 no. 8 2003
Pages 1019-1026
© 2003 Oxford University Press
Observing and interpreting correlations in metabolomic networks
1 University Potsdam,
Nonlinear Dynamics Group, Am Neuen Palais 10, 14469 Potsdam
2 Max-Planck-Institute for Molecular Plant Physiology,
Am Mühlenberg 1, 14476 Golm, Germany
Received on September 17, 2002
; revised on December 3, 2002
; accepted on January 14, 2002
Motivation: Metabolite profiling aims at an unbiased identification and quantification of all the metabolites present in a biological sample. Based on their pair-wise correlations, the data obtained from metabolomic experiments are organized into metabolic correlation networks and the key challenge is to deduce unknown pathways based on the observed correlations. However, the data generated is fundamentally different from traditional biological measurements and thus the analysis is often restricted to rather pragmatic approaches, such as data mining tools, to discriminate between different metabolic phenotypes.
Methods and Results: We investigate to what extent the data generated networks reflect the structure of the underlying biochemical pathways. The purpose of this work is 2-fold: Based on the theory of stochastic systems, we first introduce a framework which shows that the emergent correlations can be interpreted as a ‘fingerprint’ of the underlying biophysical system. This result leads to a systematic relationship between observed correlation networks and the underlying biochemical pathways. In a second step, we investigate to what extent our result is applicable to the problem of reverse engineering, i.e. to recover the underlying enzymatic reaction network from data. The implications of our findings for other bioinformatics approaches are discussed.
Contact: steuer{at}agnld.uni-potsdam.de
* To whom correspondence should be addressed.
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