Modelling signalling networks from perturbation data

Bioinformatics. 2018 Dec 1;34(23):4079-4086. doi: 10.1093/bioinformatics/bty473.

Abstract

Motivation: Intracellular signalling is realized by complex signalling networks, which are almost impossible to understand without network models, especially if feedbacks are involved. Modular Response Analysis (MRA) is a convenient modelling method to study signalling networks in various contexts.

Results: We developed the software package STASNet (STeady-STate Analysis of Signalling Networks) that provides an augmented and extended version of MRA suited to model signalling networks from incomplete perturbation schemes and multi-perturbation data. Using data from the Dialogue on Reverse Engineering Assessment and Methods challenge, we show that predictions from STASNet models are among the top-performing methods. We applied the method to study the effect of SHP2, a protein that has been implicated in resistance to targeted therapy in colon cancer, using a novel dataset from the colon cancer cell line Widr and a SHP2-depleted derivative. We find that SHP2 is required for mitogen-activated protein kinase signalling, whereas AKT signalling only partially depends on SHP2.

Availability and implementation: An R-package is available at https://github.com/molsysbio/STASNet.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Line, Tumor
  • Colonic Neoplasms
  • Computational Biology
  • Humans
  • Protein Tyrosine Phosphatase, Non-Receptor Type 11 / genetics
  • Signal Transduction*
  • Software*

Substances

  • Protein Tyrosine Phosphatase, Non-Receptor Type 11