2007/06/13 by Ilya Nemenman, G. Sean Escola, William S. Hlavacek +4 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Gene Regulatory Network Analysis #Microbial Metabolic Engineering and Bioproduction #q-bio.MN
paper · pdf · doi:10.1196/annals.1407.013
published as Ann. N.Y. Acad. Sci. 1115: 102â?"115 (2007) · 14 pages, 3 figures. Presented at the DIMACS Workshop on Dialogue on Reverse Engineering Assessment and Methods (DREAM), Sep 2006
arxiv created 2007/06/13 · openalex publication_date 2007/11/16 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.