Our knowledge about the biochemical reaction taking place in a living cell has increased to a point where we are able to map large parts of their metabolic network. Moreover, advanced simulation techniques enable us to simulate the growth of a cell on the metabolic level. To grasp the topology of the metabolic network and to interpret the fluxes through its reactions a visualization of the simulation results is indispensable. However, traditional graph visualizations are unable to cope with the complexity of metabolic networks and often very cluttered graphs. In contrast, AMEBA reduces the number of nodes displayed while retaining as much information as possible by identifying branch points in the metabolic network. Although the algorithm does not rely on extrinsic information, it can be guided additionally by expert knowledge to produce camera-ready figures. Furthermore, the run-time of less than a second -- even for large metabolic networks -- enables an interactive usage. This way, a researcher can track the fate of compounds through the whole network and thus is able to get the big picture. The development of AMEBA was tremendously facilitated by the availability of FLOSS. In fact, the first prototype was created within a week and subsequently refined to a stable analysis tool, which is now available under the terms of the GPL (http://metano.tu-bs.de/ameba). The talk will cover the details of the algorithm and will highlight the steps at which FLOSS was crucial for the success of this piece of scientific software.