title: Bayesian inference of signaling network topology in a cancer cell line.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  http://mukherjeelab.nki.nl/DBN (code and data).
authors.affiliation: Department of Biochemistry, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Hill
authors.forename: Yiling
authors.initials: Y
authors.lastname: Lu
authors.forename: Jennifer
authors.initials: J
authors.lastname: Molina
authors.forename: Laura M
authors.initials: LM
authors.lastname: Heiser
authors.forename: Paul T
authors.initials: PT
authors.lastname: Spellman
authors.forename: Terence P
authors.initials: TP
authors.lastname: Speed
authors.forename: Joe W
authors.initials: JW
authors.lastname: Gray
authors.forename: Gordon B
authors.initials: GB
authors.lastname: Mills
authors.forename: Sach
authors.initials: S
authors.lastname: Mukherjee
mesh_terms: D019540:Area Under Curve; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000378:metabolism* / Q000473:pathology; D002450:Cell Communication; D045744:Cell Line, Tumor; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008958:Models, Molecular; D015233:Models, Statistical; D011336:Probability; D015398:Signal Transduction