Bayesian Inference of Signaling Network Topology in a Cancer Cell Line
- Hill, SM; Lu, YL; Molina, J; Heiser, LM; Spellman, PT; Speed, TP; Gray, JW; Mills, GB; Mukherjee, S;
Publication Year 2012-11-01, Volume 28, Issue #21, Page 2804-2810
- Journal Title
- Publication Type
- Journal Article
- Motivation: Protein signaling networks play a key role in cellular function, and their dysregulation is central to many diseases, including cancer. To shed light on signaling network topology in specific contexts, such as cancer, requires interrogation of multiple proteins through time and statistical approaches to make inferences regarding network structure. Results: In this study, we use dynamic Bayesian networks to make inferences regarding network structure and thereby generate testable hypotheses. We incorporate existing biology using informative network priors, weighted objectively by an empirical Bayes approach, and exploit a connection between variable selection and network inference to enable exact calculation of posterior probabilities of interest. The approach is computationally efficient and essentially free of user-set tuning parameters. Results on data where the true, underlying network is known place the approach favorably relative to existing approaches. We apply these methods to reverse-phase protein array time-course data from a breast cancer cell line (MDA-MB-468) to predict signaling links that we independently validate using targeted inhibition. The methods proposed offer a general approach by which to elucidate molecular networks specific to biological context, including, but not limited to, human cancers.
- OXFORD UNIV PRESS
- GENE REGULATORY NETWORKS; VARIABLE SELECTION; EXPRESSION DATA; PRIOR KNOWLEDGE; PROTEIN ARRAY; PATHWAYS; MODELS; LASSO
- WEHI Research Division(s)
- Link To PubMed Central Version
- Publisher's Version
- Rights Notice
- © The Author 2012. Published by Oxford University Press. All rights reserved.
Creation Date: 2012-11-01 12:00:00Last Modified: 0001-01-01 12:00:00