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

Modelling signalling networks from perturbation data.

Author information

1
Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.
2
IRI Life Sciences, Humboldt University of Berlin, Berlin, Germany.
3
Berlin Institute of Health, Berlin, Germany.
4
Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, CX, Amsterdam, The Netherlands.

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.

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