Work

Reconstruction of Metabolic Networks from High‐Throughput Metabolite Profiling Data

Year: 2007

Type: article

Abstract: We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well-established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.

Source: Annals of the New York Academy of Sciences

DOI: 10.1196/annals.1407.013

Authors: ILYA NEMENMAN, G. SEAN ESCOLA, WILLIAM S. HLAVACEK, PAT J. UNKEFER, CLIFFORD J. UNKEFER, MICHAEL E. WALL

Institutions: Los Alamos National Laboratory, Columbia University Irving Medical Center

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Institute of General Medical Sciences

Awards: G8145560422, System-wide Study of Transcriptional Control of Metabolism