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Studies in many areas of biology are hampered by the complexity of the system being studied. This suggests that such areas of study could benefit from the development and application of new and more powerful analytical tools. Traditionally, investigators have chosen analytical methods that offer sensitivity and specificity, ultimately fitting collected results into an overall global picture or an evolving theory. This approach has been effective for the study of disorders manifested by a single etiology or disorders where prior knowledge indicates a clear or probable mechanism. The approach has proven less satisfying, however, when applied to complex systems with many unknown influences. Factors such as the time and cost involved in sorting through myriad possibilities as well as biological constraints (e.g., sample-size constraints) have limited analysis carried out in such complex systems. A related, although distinct, problem lies in the inability to simultaneously measure a series of related metabolites even after the relevance of the metabolic system has been identified.