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We present a step towards the metabolome-wide computational inference ofcellular metabolic reaction networks from metabolic profiling data, such asmass spectrometry. The reconstruction is based on identification of irreduciblestatistical interactions among the metabolite activities using the ARACNEreverse-engineering algorithm and on constraining possible metabolictransformations to satisfy the conservation of mass. The resulting algorithmsare validated on synthetic data from an abridged computational model ofEscherichia coli metabolism. Precision rates upwards of 50% are routinelyobserved for identification of full metabolic reactions, and recalls upwards of20% are also seen.

Mass Conservation And Inference of Metabolic Networks from High-throughput Mass Spectrometry Data