Metabolic networks consist of interacting biochemical reaction chains (i.e., pathways) that are responsible for essential cellular processes such as energy production, DNA synthesis, and lipid breakdown. Reactions in these networks are connected to each other through chemical substances (e.g., glucose) that they consume and produce. Such substances are called “metabolites”, and with the recent advancements in biotechnology, it is possible to simultaneously measure the concentrations for thousands of metabolites in biofluids (e.g., blood, urine, etc.). Extraordinary changes in metabolite concentrations often point to physiological conditions. Metabolomics is the study of these concentration changes as well as interpretation of what their biochemical implications may be. Interpreting the concentration changes of large numbers of metabolites with respect to a metabolic network is complicated, time consuming, and cumbersome. In order to decrease and manage the complexity, most researchers usually focus on only a small select set of metabolites among thousands. This usually results in omitting a large chunk of the big picture, and makes the analysis conclusions local and limited. The goal of this project is to develop models, algorithms, and tools to automatically interpret large numbers of metabolite concentration changes in a holistic manner, and produce a set of biologically viable hypotheses explaining the observed changes. Besides, we aim to create health applications that will enable the use of the output from the above models and algorithms.