CNORfuzzy (version 1.14.0)

CNORfuzzy-package: R version of CNOFuzzy, a Constrained Fuzzy Logic Network Optimisation

Description

This package does optimisation of constrained Fuzzy logic networks of signalling pathways based on a previous knowledge network and a set of data collected upon perturbation of some of the nodes in the network.

Details

Package:

CNOR

Type:

Package

Version:

1.4.0

Date:

2013-08-28

License:

GPL-2

LazyLoad:

yes

Depends:

R (>= 2.15.0), CellNOptR (>= 1.3.29), nloptr (>= 0.8.5)

References

1. J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. Lauffenburger, S. Klamt and P. K. Sorger. Discrete logic modeling as a means to link protein signaling networks with functional analysis of mammalian signal transduction, Molecular Systems Biology, 5:331, 2009.
2. Morris MK, Saez-Rodriguez J, Clarke DC, Sorger PK, Lauffenburger DA (2011). Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli. PLoS Comput Biol. 7(3): e1001099.

See Also

CellNOptR package.

Examples

    # Get data from CellNOptR package
    data(CNOlistToy,package="CellNOptR")
    data(ToyModel,package="CellNOptR")

    # Use the default parameters and set Data and Model
    paramsList=defaultParametersFuzzy()
    paramsList$data<-CNOlistToy
    paramsList$model<-ToyModel

## Not run: 
#     # Run the simulator
#     Res = CNORwrapFuzzy(data=CNOlistToy, model=ToyModel, paramsList=paramsList)
#     ## End(Not run)