Pathway discovery in metabolic networks by subgraph extraction

Bioinformatics. 2010 May 1;26(9):1211-8. doi: 10.1093/bioinformatics/btq105. Epub 2010 Mar 12.


Motivation: Subgraph extraction is a powerful technique to predict pathways from biological networks and a set of query items (e.g. genes, proteins, compounds, etc.). It can be applied to a variety of different data types, such as gene expression, protein levels, operons or phylogenetic profiles. In this article, we investigate different approaches to extract relevant pathways from metabolic networks. Although these approaches have been adapted to metabolic networks, they are generic enough to be adjusted to other biological networks as well.

Results: We comparatively evaluated seven sub-network extraction approaches on 71 known metabolic pathways from Saccharomyces cerevisiae and a metabolic network obtained from MetaCyc. The best performing approach is a novel hybrid strategy, which combines a random walk-based reduction of the graph with a shortest paths-based algorithm, and which recovers the reference pathways with an accuracy of approximately 77%.

Availability: Most of the presented algorithms are available as part of the network analysis tool set (NeAT). The kWalks method is released under the GPL3 license.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adenosine Triphosphate / metabolism
  • Algorithms
  • Computational Biology / methods*
  • Escherichia coli / metabolism
  • Gene Expression Profiling
  • Gene Expression Regulation / physiology*
  • Metabolic Networks and Pathways
  • Models, Biological
  • Models, Theoretical
  • NADP / metabolism
  • Phylogeny
  • Probability
  • Reproducibility of Results
  • Saccharomyces cerevisiae / genetics*
  • Water / chemistry


  • Water
  • NADP
  • Adenosine Triphosphate