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

TL;DR

This paper introduces a computational method to infer cellular metabolic networks from high-throughput mass spectrometry data by combining statistical interaction analysis with mass conservation constraints, validated on synthetic E. coli data.

Contribution

It presents a novel approach integrating ARACNE-based interaction detection with mass conservation constraints for metabolic network inference from profiling data.

Findings

Achieved over 50% precision in identifying metabolic reactions

Attained over 20% recall in network reconstruction

Validated method on synthetic E. coli metabolic 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.