Title: Learning robust cell signalling models from high throughput proteomic data

Authors: Mitchell Koch, Bradley M. Broom, Devika Subramanian

Addresses: Department of Computer Science, Rice University, Houston, TX 77005, USA. ' Department of Bioinformatics and Computational Biology, M.D. Anderson Cancer Center, Houston, TX 77030, USA. ' Department of Computer Science, Rice University, Houston, TX 77005, USA

Abstract: We propose a framework for learning robust Bayesian network models of cell signalling from high-throughput proteomic data. We show that model averaging using Bayesian bootstrap resampling generates more robust structures than procedures that learn structures using all of the data. We also develop an algorithm for ranking the importance of network features using bootstrap resample data. We apply our algorithms to derive the T-cell signalling network from the flow cytometry data of Sachs et al. (2005). Our learning algorithm has identified, with high confidence, several new crosstalk mechanisms in the T-cell signalling network. Many of them have already been confirmed experimentally in the recent literature and six new crosstalk mechanisms await experimental validation.

Keywords: systems biology; Bayesian networks; T-cell signalling; crosstalk; flow cytometry; bioinformatics; cell signalling models; proteomic data.

DOI: 10.1504/IJBRA.2009.026417

International Journal of Bioinformatics Research and Applications, 2009 Vol.5 No.3, pp.241 - 253

Published online: 11 Jun 2009 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article