A comprehensive statistical model for cell signaling

Erdem Yörük, Michael F. Ochs, Donald Geman, Laurent Younes

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Article number5582072
Pages (from-to)592-606
Number of pages15
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number3
StatePublished - 2011


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

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics
  • Medicine(all)


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