Inferring signalling dynamics by integrating interventional with observational data
![Thumbnail](/bitstream/handle/20.500.11850/354244/btz325.pdf.jpg?sequence=8&isAllowed=y)
Open access
Date
2019-07Type
- Journal Article
Abstract
Motivation
In order to infer a cell signalling network, we generally need interventional data from perturbation experiments. If the perturbation experiments are time-resolved, then signal progression through the network can be inferred. However, such designs are infeasible for large signalling networks, where it is more common to have steady-state perturbation data on the one hand, and a non-interventional time series on the other. Such was the design in a recent experiment investigating the coordination of epithelial–mesenchymal transition (EMT) in murine mammary gland cells. We aimed to infer the underlying signalling network of transcription factors and microRNAs coordinating EMT, as well as the signal progression during EMT.
Results
In the context of nested effects models, we developed a method for integrating perturbation data with a non-interventional time series. We applied the model to RNA sequencing data obtained from an EMT experiment. Part of the network inferred from RNA interference was validated experimentally using luciferase reporter assays. Our model extension is formulated as an integer linear programme, which can be solved efficiently using heuristic algorithms. This extension allowed us to infer the signal progression through the network during an EMT time course, and thereby assess when each regulator is necessary for EMT to advance. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000354244Publication status
publishedExternal links
Journal / series
BioinformaticsVolume
Pages / Article No.
Publisher
Oxford University PressOrganisational unit
03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
More
Show all metadata