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Functional Alignment of Metabolic Networks

  • Arnon MazzaEmail author
  • Allon Wagner
  • Eytan Ruppin
  • Roded Sharan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9029)

Abstract

Network alignment has become a standard tool in comparative biology, allowing the inference of protein function, interaction and orthology. However, current alignment techniques are based on topological properties of networks and do not take into account their functional implications. Here we propose, for the first time, an algorithm to align two metabolic networks by taking advantage of their coupled metabolic models. These models allow us to assess the functional implications of genes or reactions, captured by the metabolic fluxes that are altered following their deletion from the network. Such implications may spread far beyond the region of the network where the gene or reaction lies. We apply our algorithm to align metabolic networks from various organisms, ranging from bacteria to humans, showing that our alignment can reveal functional orthology relations that are missed by conventional topological alignments.

Keywords

Metabolic Network Alignment Algorithm Metabolic Model Flux Balance Analysis Node Similarity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Arnon Mazza
    • 1
    Email author
  • Allon Wagner
    • 1
    • 2
  • Eytan Ruppin
    • 1
    • 3
    • 4
  • Roded Sharan
    • 1
  1. 1.Blavatnik School of Computer ScienceTel Aviv UniversityTel AvivIsrael
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of CaliforniaBerkeleyUSA
  3. 3.The Sackler School of MedicineTel Aviv UniversityTel AvivIsrael
  4. 4.Department of Computer Science, Institute of Advanced Computer Sciences (UMIACS) & the Center for Bioinformatics and Computational BiologyUniversity of MarylandCollege ParkUSA

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