Publication Details
Title: Reconstructing Boolean Models of Signaling
Author: R. Sharan and R. M. Karp
Group: Algorithms
Date: April 2012
PDF: [Not available online]
Overview:
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.
Bibliographic Information:
Proceedings of the 16th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2012), Barcelona, Spain, pp. 261-271
Bibliographic Reference:
R. Sharan and R. M. Karp. Reconstructing Boolean Models of Signaling. Proceedings of the 16th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2012), Barcelona, Spain, pp. 261-271, April 2012
Author: R. Sharan and R. M. Karp
Group: Algorithms
Date: April 2012
PDF: [Not available online]
Overview:
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.
Bibliographic Information:
Proceedings of the 16th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2012), Barcelona, Spain, pp. 261-271
Bibliographic Reference:
R. Sharan and R. M. Karp. Reconstructing Boolean Models of Signaling. Proceedings of the 16th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2012), Barcelona, Spain, pp. 261-271, April 2012