A Minimum-Labeling Approach for Reconstructing Protein Networks across Multiple Conditions

  • Arnon Mazza
  • Irit Gat-Viks
  • Hesso Farhan
  • Roded Sharan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8126)


The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to facilitate the interpretation and representation of these data. A fundamental challenge in this domain is the reconstruction of a protein-protein subnetwork that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorithmic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question. Here we propose a novel formulation for network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza infection in humans over time as well as to analyze a pair of ER export related screens in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes.


Integer Linear Program Steiner Tree Anchor Node Integer Linear Program Formulation Network Analysis Tool 
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 2013

Authors and Affiliations

  • Arnon Mazza
    • 1
  • Irit Gat-Viks
    • 2
  • Hesso Farhan
    • 3
  • Roded Sharan
    • 1
  1. 1.Blavatnik School of Computer ScienceTel Aviv UniversityTel AvivIsrael
  2. 2.Dept. of Cell Research and ImmunologyTel Aviv UniversityTel AvivIsrael
  3. 3.Biotechnology Institute ThurgauUniversity of KonstanzKreuzlingenSwitzerland

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