Abstract
With recent advances in experimental technologies, the number of metabolites measured in bio-fluids of organisms has markedly increased. Given a set of measurements, a common metabolomics task is to identify the metabolic mechanisms that lead to changes in the concentrations of given metabolites, and interpret the metabolic consequences of the observed changes in terms of physiological problems, nutritional deficiencies, or diseases.This paper presents the SMDA (steady-state metabolic network dynamics analysis) technique and its computational performance limits using a mammalian metabolic network database. The query output space of the SMDA tool is exponentially large in the number of reactions of the network. However, (i) larger numbers of observations exponentially reduce the output size, and (ii) exploratory search and browsing of the query output space allows users to mine and search for what they are looking for.
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