Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

Daniel Morgan, Matthew Studham, Andreas Tjärnberg, Holger Weishaupt, Fredrik J. Swartling, Torbjörn E.M. Nordling, Erik L.L. Sonnhammer

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.

Original languageEnglish
Article number14149
JournalScientific reports
Issue number1
Publication statusPublished - 2020 Dec 1

All Science Journal Classification (ASJC) codes

  • General


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