Lagrangian Relaxation Applied to Sparse Global Network Alignment

  • Mohammed El-Kebir
  • Jaap Heringa
  • Gunnar W. Klau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)


Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is lagging behind. This holds in particular for the field of comparative network analysis, where one wants to identify commonalities between biological networks. Since biological functionality primarily operates at the network level, there is a clear need for topology-aware comparison methods. In this paper we present a method for global network alignment that is fast and robust, and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account. It is based on an integer linear programming formulation, generalizing the well-studied quadratic assignment problem. We obtain strong upper and lower bounds for the problem by improving a Lagrangian relaxation approach and introduce the software tool natalie 2.0, a publicly available implementation of our method. In an extensive computational study on protein interaction networks for six different species, we find that our new method outperforms alternative state-of-the-art methods with respect to quality and running time. An extended version of this paper including proofs and pseudo code is available at .


Integer Linear Programming Protein Protein Interaction Network Quadratic Assignment Problem Optimal Alignment Integer Linear Programming Formulation 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohammed El-Kebir
    • 1
    • 2
    • 3
  • Jaap Heringa
    • 2
    • 3
    • 4
  • Gunnar W. Klau
    • 1
    • 3
  1. 1.Life Sciences GroupCentrum Wiskunde & InformaticaAmsterdamThe Netherlands
  2. 2.Centre for Integrative Bioinformatics VU (IBIVU)VU University AmsterdamAmsterdamThe Netherlands
  3. 3.Netherlands Institute for Systems BiologyAmsterdamThe Netherlands
  4. 4.Netherlands Bioinformatics CentreThe Netherlands

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