saveNetwork: Save a network

In BoolNet: Construction, Simulation and Analysis of Boolean Networks

Save a network

Description

Saves synchronous, asynchronous, probabilistic and temporal networks in the BoolNet network file format .

Usage

saveNetwork(network, 
            file, 
            generateDNFs = FALSE,
            saveFixed = TRUE)

Arguments

network	An object of class BooleanNetwork or SymbolicBooleanNetwork to be exported
file	The name of the network file to be created
generateDNFs	If network is a BooleanNetwork object, this parameter specifies whether formulae in Disjunctive Normal Form are exported instead of the expressions that describe the transition functions. If set to FALSE, the original expressions are exported. If set to "canonical", a canonical Disjunctive Normal Form is generated from each truth table. If set to "short", the canonical DNF is minimized by joining terms (which can be time-consuming for functions with many inputs). If set to TRUE, a short DNF is generated for functions with up to 12 inputs, and a canonical DNF is generated for functions with more than 12 inputs. For objects of class SymbolicBooleanNetwork, this parameter is ignored.
saveFixed	If set to TRUE, knock-outs and overexpression of genes override their transition functions. That is, if a gene in the network is fixed to 0 or 1, this value is saved, regardless of the transition function. If set to FALSE, the transition function is saved. Defaults to TRUE.

Details

The network is saved in the BoolNet file format (see loadNetwork for details).

If the expressions in the transition functions cannot be parsed or generateDNFs is true, a DNF representation of the transition functions is generated.

See Also

loadNetwork

Examples

## Not run: 
# load the cell cycle network
data(cellcycle)

# save it to a file
saveNetwork(cellcycle, file="cellcycle.txt")

# reload the model
print(loadNetwork("cellcycle.txt"))

## End(Not run)

BoolNet documentation built on Oct. 2, 2023, 5:08 p.m.