title: Mass conservation and inference of metabolic networks from high-throughput mass spectrometry data.
abstract: 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.
authors.affiliation: Center for Computational Biology and Bioinformatics, Joint Centers for Systems Biology, and Columbia Initiative in Systems Biology, Columbia University, New York, New York, USA.
authors.forename: Pradeep
authors.initials: P
authors.lastname: Bandaru
authors.forename: Mukesh
authors.initials: M
authors.lastname: Bansal
authors.forename: Ilya
authors.initials: I
authors.lastname: Nemenman
mesh_terms: D000465:Algorithms; D019992:Databases as Topic; D004926:Escherichia coli / Q000378:metabolism*; D057166:High-Throughput Screening Assays / Q000379:methods*; D013058:Mass Spectrometry / Q000379:methods*; D053858:Metabolic Networks and Pathways; D055442:Metabolome