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.

In this paper we address the problem of identifying all nondominated criterion vectors in large, finite sets of criterion vectors. Two methods, quad-trees and linear lists, are studied in detail. In discussing the methods, a complete algorithmic description of the quad-tree approach, which builds upon the description given by Habenicht (W. Habenicht, ENUQUAD, an interactive DSS-tool for discrete vector optimization problems, M. Cerny, D. Glückaufova, D. Loula, eds. Multicriteria Decision Making: Methods–Algorithms–Applications, Institute of Economics, Prague, pp. 66–81, 1992.), is specified and computational results are reported. The results show that the quad-tree approach is faster than the linear list approach on problems with large sets of criterion vectors that have more than 2, but fewer than 8, objectives. Also, in general, the larger the proportion of vectors that are nondominated in the set of criterion vectors, the more effective the quad-tree approach.