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Andreas Hoppe (Charité Berlin, Berlin, Germany)The cellular metabolism is traditionally displayed as a number of metabolic pathways, and interactions across pathways are usually not explicitly drawn. However, proteomics or transcriptomics data show patterns of expressed enzymes that often do not follow conventional pathways. A clear graphical representation of large scale networks (>1000 reactions) as a whole is an infeasible task. However, flux distributions for specific metabolic objectives predicted by FBA (MinModes) with a more reasonable size (<200 reactions) are more suited for meaningful graphs. The visualization tool BiNA combined with FBA software developed by the authors is ideally suited to visualize MinModes: BiNA provides state-of-the-art automatic graph-layout algorithms in comprehensive, interactive network visualization. Flux rates, concentration values or expression data can be represented e.g. by arrow-thickness and colors. Cellular compartmentalization is either reflected by network topology or by color. Here, we present a work flow: from the prediction of flux distributions to a testable hypothesis on a previously unknown interaction pattern of enzymes using automatic layout, manual refinement and manu