CellNOptR in Cytoscape

CytoCopteR provides an intuitive and easy to learn graphical user interface (GUI) to CellNOptR methods through Cytoscape.

This results in a point and click interface where users can run the same steps as they would using an R script without having to actually write any code. Given that this is a front-end to the R algorithms, consistency is ensured between the results obtained through the GUI and those obtained through the corresponding scripts.

Download

Software requirements: Cytoscape v3.1 and R v3.x

Cytocopter can be installed from the Cytoscape App Manager:

Within Cytoscape click Apps -> App Manager and then search and install first Cyrface then Cytocopter

Or by downloading manually Cyrface and Cytocopter jar files and placing them within the installed Apps folder:

Cytocopter v2.0 (26 MB) Latest version (09/05/2014)

Cyrface v2.0 (13 MB) Latest version (09/05/2014)

Move the Jar files into '~/CytoscapeConfiguration/3/apps/installed/' folder and start Cytoscape.

IMPORTANT: Before using Cytocopter on Ubuntu you need to type on Terminal:

sudo apt-get install libcairo2-dev
sudo apt-get install libxt-dev

Cytocopter is also available in GitHub and at Cytoscape App Store.

Demonstration

Small demonstration highlighting the basic features of Cytocopter

To illustrate the use of Cytocopter we will use a biologically plausible prior knowledge network (PKN). This network includes a subset of intracellular signaling networks known to be activated downstream of EGF and TNFa stimulation. The network model is available for download in BioModels data-base BioModels ("Download SBML" and then "SBML V3 L1").

The accompanying in silico data (MIDAS file format) replicates biologically plausible behavior that has been seen in such networks, such as the transient behavior of ERK activation and the oscillatory dynamics of NFkB translocation from the cytoplasm to the nucleus.

Data: MIDAS

Network: Sif or SBML-Qual

Cytocopter Tutorial is available here: Cytocopter Tutorial

An additional scripting tutorial demonstrating the use of logic models with increasing levels of detail is available here: R Script Tutorial