Completing SBGN-AF Networks by Logic-Based Hypothesis Finding

  • Yoshitaka Yamamoto
  • Adrien Rougny
  • Hidetomo Nabeshima
  • Katsumi Inoue
  • Hisao Moriya
  • Christine Froidevaux
  • Koji Iwanuma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8738)


This study considers formal methods for finding unknown interactions of incomplete molecular networks using microarray profiles. In systems biology, a challenging problem lies in the growing scale and complexity of molecular networks. Along with high-throughput experimental tools, it is not straightforward to reconstruct huge and complicated networks using observed data by hand. Thus, we address the completion problem of our target networks represented by a standard markup language, called SBGN (in particular, Activity Flow). Our proposed method is based on logic-based hypothesis finding techniques; given an input SBGN network and its profile data, missing interactions can be logically generated as hypotheses by the proposed method. In this paper, we also show empirical results that demonstrate how the proposed method works with a real network involved in the glucose repression of S. cerevisiae.


completion hypothesis finding SBGN glucose repression 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yoshitaka Yamamoto
    • 1
  • Adrien Rougny
    • 2
  • Hidetomo Nabeshima
    • 1
  • Katsumi Inoue
    • 3
  • Hisao Moriya
    • 4
  • Christine Froidevaux
    • 2
  • Koji Iwanuma
    • 1
  1. 1.University of YamanashiKofu-shiJapan
  2. 2.Laboratoire de Recherche en Informatique (LRI), CNRS UMR 8623Université Paris SudFrance
  3. 3.National Institute of InformaticsChiyoda-kuJapan
  4. 4.RCISOkayama UniversityOkayamaJapan

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