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An open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.

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CellNOpt specifications

Software type:
Package, Pipeline
Restrictions to use:
None
Input format:
SIF, MIDAS
Output format:
HTML
Programming languages:
MATLAB, Python, R
Computer skills:
Advanced
Stability:
Stable
Interface:
Command line interface
Input data:
A prior knowledge network (PKN) describing signed and directed interactions between proteins as a graph, biochemical data relating to the changes in the modification state (typically phosphorylation) of proteins following stimulation under various conditions
Output data:
The summary of the analysis, hyperlinked to diagnostic graphs, and the trained networks
Operating system:
Unix/Linux, Mac OS, Windows
License:
GNU General Public License version 3.0
Version:
1.20.0
Requirements:
Graphviz, R, RBGL, graph, methods, hash, ggplot2, RCurl, Rgraphviz, XML
Source code URL:
https://github.com/cellnopt/CellNOptR.git

CellNOpt support

Documentation

Credits

Publications

Institution(s)

European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK; Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, MA, USA

Funding source(s)

This work was supported by the Institute for Collaborative Biotechnologies (contract no. W911NF-09-D-0001 from the U.S. Army Research Office), EU-7FP-BioPreDyn and the EMBL EIPOD program.

Link to literature

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