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Every year since the article “How Will Big Pictures Emerge From a Sea of Biological Data?” appeared in Science, the question becomes more compelling. We are now accumulating information about biological sequences, structures, and interactions faster than we have the power to make sense of them. For hundreds of years prior to this, practical considerations coerced biological research into reductionism. There are simply too many components in a biological system for a biologist to examine the whole picture with the tools formerly available. Over the past decade this has rapidly changed as biological information has become cheap and plentiful due to the advent of high-throughput tools, making it possible for the frst time to ask questions on time and length scales that were previously intractable. The relaxation of the practical limitations on systems-level analysis has also brought a change in the philosophy of how we regard biology, moving towards a holistic method of research and interpretation. This places systems biology in stark contrast to traditional biological research, and for good reason. In the words of Denis Noble, “Systems biology is about putting together rather than taking apart, integration rather than reduction. It starts with what we have learned from the reductionist approach; and then it goes further.” This shift from reductionism is essential, for as we know from studying complex systems, the whole is greater than the sum of the parts. With this new approach we are able to explore scientifc territory that has previously been untouched due to physical impossibility and philosophical differences. The complexity of the tangled web of nonlinear interactions between genes, proteins, and the environment necessitates the development of simplifed models to illuminate biological functions. Merely generating networks of interactions is not enough, providing us with far too much information in a single view without emphasizin- the important features of the map. When we use Google maps and look at a picture of the United States it doesn't show us every city, we would never see Evanston, Illinois being shown at that level of detail. Only large and recognizable cities are shown to help us orient the map. Once we zoom in other smaller cities and features become visible, giving us more relevant information in a manner that is usable. Simply generating networks without any type of analysis or visualization is akin to showing a map of the United States with every state, city, and town marked on it. In my talk, I will describe the advances we have made in developing new visualization methods and the challenges still remaining.