Reconstruction of Metabolic Networks from High-Throughput Metabolite Profiling Data

In Silico Analysis of Red Blood Cell Metabolism

  1. ILYA NEMENMAN1,2,
  2. G. SEAN ESCOLA1,3,
  3. WILLIAM S. HLAVACEK2,4,
  4. PAT J. UNKEFER5,
  5. CLIFFORD J. UNKEFER5,
  6. MICHAEL E. WALL1,2,5

Article first published online: 16 NOV 2007

DOI: 10.1196/annals.1407.013

Additional Information

How to Cite

NEMENMAN, I., ESCOLA, G. S., HLAVACEK, W. S., UNKEFER, P. J., UNKEFER, C. J. and WALL, M. E. (2007), Reconstruction of Metabolic Networks from High-Throughput Metabolite Profiling Data. Annals of the New York Academy of Sciences, 1115: 102–115. doi: 10.1196/annals.1407.013

Author Information

  1. 1

    Computer, Computational and Statistical Sciences Division, Los Alamos

  2. 2

    Center for Nonlinear Studies, National Laboratory

  3. 3

    Columbia University Medical Center, New York, New York 10032, USA

  4. 4

    Theoretical Division, Los Alamos, New Maxico 87545, USA

  5. 5

    Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

*Addresses for correspondence: Ilya Nemenman, CCS-3, MS-B256, Los Alamos National Laboratory, Los Alamos, NM 87545. Voice: 505-665-8250; fax: 505-667-1126. Michael E. Wall, CCS-3, MS-B256, Los Alamos National Laboratory, Los Alamos, NM 87545. Voice: 505-665-4209; fax: 505-667-1126.  nemenman@lanl.gov; mewall@lanl.gov

Publication History

  1. Issue published online: 16 NOV 2007
  2. Article first published online: 16 NOV 2007

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Keywords:

  • metabolism;
  • network reverse engineering;
  • red blood cells;
  • synthetic data

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.

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