Bayesian Inference of Signaling Network Topology in a Cancer Cell Line

Steven M. Hill, Yiling Lu, Jennifer Molina, Laura Heiser, Paul Spellman, Terence P. Speed, Joe Gray, Gordon Mills, Sach Mukherjee

Research output: Contribution to journalArticle

59 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)2804-2810
Number of pages7
JournalBioinformatics
Volume28
Issue number21
DOIs
StatePublished - Nov 2012

Fingerprint

Bayesian inference
Network Topology
Cancer
Cells
Topology
Proteins
Cell Line
Line
Cell
Neoplasms
Protein Array Analysis
Protein
Network Structure
Bayesian networks
Tuning
Dynamic Bayesian Networks
Empirical Bayes
Breast Neoplasms
Posterior Probability
Parameter Tuning

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability
  • Medicine(all)

Cite this

Bayesian Inference of Signaling Network Topology in a Cancer Cell Line. / Hill, Steven M.; Lu, Yiling; Molina, Jennifer; Heiser, Laura; Spellman, Paul; Speed, Terence P.; Gray, Joe; Mills, Gordon; Mukherjee, Sach.

In: Bioinformatics, Vol. 28, No. 21, 11.2012, p. 2804-2810.

Research output: Contribution to journalArticle

Hill, Steven M. ; Lu, Yiling ; Molina, Jennifer ; Heiser, Laura ; Spellman, Paul ; Speed, Terence P. ; Gray, Joe ; Mills, Gordon ; Mukherjee, Sach. / Bayesian Inference of Signaling Network Topology in a Cancer Cell Line. In: Bioinformatics. 2012 ; Vol. 28, No. 21. pp. 2804-2810.
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