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Nested effects models for learning signaling networks from perturbation data
Author(s) -
Fröhlich Holger,
Tresch Achim,
Beißbarth Tim
Publication year - 2009
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200800185
Subject(s) - computational biology , dna microarray , biological network , computer science , probabilistic logic , systems biology , gene , biology , theoretical computer science , artificial intelligence , genetics , gene expression
Targeted gene perturbations have become a major tool to gain insight into complex cellular processes. In combination with the measurement of downstream effects via DNA microarrays, this approach can be used to gain insight into signaling pathways. Nested Effects Models were first introduced by Markowetz et al . as a probabilistic method to reverse engineer signaling cascades based on the nested structure of downstream perturbation effects. The basic framework was substantially extended later on by Fröhlich et al ., Markowetz et al ., and Tresch and Markowetz. In this paper, we present a review of the complete methodology with a detailed comparison of so far proposed algorithms on a qualitative and quantitative level. As an application, we present results on estimating the signaling network between 13 genes in the ER‐α pathway of human MCF‐7 breast cancer cells. Comparison with the literature shows a substantial overlap.