Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data

Nat Commun. 2015 Sep 10;6:8033. doi: 10.1038/ncomms9033.


Mass spectrometry is widely used to probe the proteome and its modifications in an untargeted manner, with unrivalled coverage. Applied to phosphoproteomics, it has tremendous potential to interrogate phospho-signalling and its therapeutic implications. However, this task is complicated by issues of undersampling of the phosphoproteome and challenges stemming from its high-content but low-sample-throughput nature. Hence, methods using such data to reconstruct signalling networks have been limited to restricted data sets and insights (for example, groups of kinases likely to be active in a sample). We propose a new method to handle high-content discovery phosphoproteomics data on perturbation by putting it in the context of kinase/phosphatase-substrate knowledge, from which we derive and train logic models. We show, on a data set obtained through perturbations of cancer cells with small-molecule inhibitors, that this method can study the targets and effects of kinase inhibitors, and reconcile insights obtained from multiple data sets, a common issue with these data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chromatography, Liquid
  • Data Interpretation, Statistical
  • Humans
  • MCF-7 Cells
  • Models, Biological
  • Models, Statistical*
  • Phosphoproteins / metabolism*
  • Phosphorylation
  • Phosphotransferases / antagonists & inhibitors*
  • Protein Kinase Inhibitors / pharmacology*
  • Proteomics / methods*
  • Signal Transduction*
  • Tandem Mass Spectrometry


  • Phosphoproteins
  • Protein Kinase Inhibitors
  • Phosphotransferases