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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

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



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.


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

Supplementary information:

Supplementary data are available at Bioinformatics online.

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