| In the late 1990s and early 2000s, the answer posed to modern research difficulties was that more time, people, and data were necessary. As scientists, we implicitly postulated that once hordes of researchers generated sufficient amounts of data the remaining biological mysteries would be conquerable. This sentiment was expressed best in the wake of celebratory publications for the human genome sequencing and the successive creation of each "ome" afterward. Sadly, the last decade has taught us that the accumulation of biological data, by itself, does not increase understanding. In fact, the aggregation, processing, and analysis of this newly synthesized data continually challenges researchers.;Here, I report on a framework to process, analyze, and visualize the deluge of data in metabolism. Our framework enables the analysis of all organismal networks simultaneously and the exploration of broad evolutionary patterns. This framework is used to predict metabolic networks, identify global network structure at an increased resolution, qualify the relationship between topology and biological usage, identify intermediate topological structures, and map the structure of metabolism in a way that provides true insight to researchers. Mapping biological systems is one of the most pressing concerns in modern systems biology and we believe that this framework will not only aid in reducing information complexity, but also make the underlying data useful in further exploration of important open questions in biology. |
