Reconstructing Boolean models of signaling

Roded Sharan*, Richard M. Karp

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Since the first emergence of protein-protein interaction networks, more than a decade ago, they have been viewed as static scaffolds of the signaling-regulatory events taking place in the cell and their analysis has been mainly confined to topological aspects. Recently, functional models of these networks have been suggested, ranging from Boolean to constraint-based ones. However, learning such models from large-scale data remains a formidable task and most modeling approaches rely on extensive human curation. Here we provide a generic approach to learning Boolean models automatically from data. We apply our approach to growth and inflammatory signaling systems in human and show how the learning phase can improve the fit of the model to experimental data, remove spurious interactions and lead to better understanding of the system at hand.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 16th Annual International Conference, RECOMB 2012, Proceedings
Number of pages11
StatePublished - 2012
Event16th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2012 - Barcelona, Spain
Duration: 21 Apr 201224 Apr 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7262 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2012


  • Boolean modeling
  • Integer linear programming
  • Signaling network


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