Completing SBGN-AF networks by logic-based hypothesis finding

Yoshitaka Yamamoto, Adrien Rougny, Hidetomo Nabeshima, Katsumi Inoue, Hisao Moriya, Christine Froidevaux, Koji Iwanuma

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationFormal Methods in Macro-Biology - First International Conference, FMMB 2014, Proceedings
EditorsCarla Piazza, François Fages
PublisherSpringer Verlag
Number of pages15
ISBN (Electronic)9783319103976
ISBN (Print)9783319103976
Publication statusPublished - 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Completion
  • Glucose repression
  • Hypothesis finding
  • SBGN

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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