Biology – Quantitative Biology – Molecular Networks
Scientific paper
2007-06-13
Ann. N.Y. Acad. Sci. 1115: 102\^a?"115 (2007)
Biology
Quantitative Biology
Molecular Networks
14 pages, 3 figures. Presented at the DIMACS Workshop on Dialogue on Reverse Engineering Assessment and Methods (DREAM), Sep 2
Scientific paper
10.1196/annals.1407.013
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 this, we generate synthetic metabolic profiles for benchmarking purposes based on a well-established model for red blood cell metabolism. A variety of data sets is 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 apply ARACNE, a mainstream transcriptional networks reverse engineering algorithm, to these data sets and observe performance comparable to that obtained in the transcriptional domain, for which the algorithm was originally designed.
Escola Sean G.
Hlavacek William S.
Nemenman Ilya
Unkefer Clifford J.
Unkefer Pat J.
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