CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2011 vol.8 Issue No.03 - May/June

Subscribe

Issue No.03 - May/June (2011 vol.8)

pp: 592-606

Erdem Yörük , The Johns Hopkins University, Baltimore

Michael F. Ochs , The Johns Hopkins University, Baltimore

Donald Geman , The Johns Hopkins University, Baltimore

Laurent Younes , The Johns Hopkins University, Baltimore

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.87

ABSTRACT

Protein signaling networks play a central role in transcriptional regulation and the etiology of many diseases. Statistical methods, particularly Bayesian networks, have been widely used to model cell signaling, mostly for model organisms and with focus on uncovering connectivity rather than inferring aberrations. Extensions to mammalian systems have not yielded compelling results, due likely to greatly increased complexity and limited proteomic measurements in vivo. In this study, we propose a comprehensive statistical model that is anchored to a predefined core topology, has a limited complexity due to parameter sharing and uses micorarray data of mRNA transcripts as the only observable components of signaling. Specifically, we account for cell heterogeneity and a multilevel process, representing signaling as a Bayesian network at the cell level, modeling measurements as ensemble averages at the tissue level, and incorporating patient-to-patient differences at the population level. Motivated by the goal of identifying individual protein abnormalities as potential therapeutical targets, we applied our method to the RAS-RAF network using a breast cancer study with 118 patients. We demonstrated rigorous statistical inference, established reproducibility through simulations and the ability to recover receptor status from available microarray data.

INDEX TERMS

Cell signaling networks, signaling protein, microarray, statistical learning, Bayesian networks, stochastic approximation expectation maximization, Gibbs sampling, Mann-Whitney-Wilcoxon test.

CITATION

Erdem Yörük, Michael F. Ochs, Donald Geman, Laurent Younes, "A Comprehensive Statistical Model for Cell Signaling",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol.8, no. 3, pp. 592-606, May/June 2011, doi:10.1109/TCBB.2010.87REFERENCES