  We present a step towards the metabolome-wide computational inference of
cellular metabolic reaction networks from metabolic profiling data, such as
mass spectrometry. The reconstruction is based on identification of irreducible
statistical interactions among the metabolite activities using the ARACNE
reverse-engineering algorithm and on constraining possible metabolic
transformations to satisfy the conservation of mass. The resulting algorithms
are validated on synthetic data from an abridged computational model of
Escherichia coli metabolism. Precision rates upwards of 50% are routinely
observed for identification of full metabolic reactions, and recalls upwards of
20% are also seen.
