gaBinaryDT: Genetic algorithm for optimizing models using multiple...

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/gaBinaryDT.R

Description

The genetic algorithm used to optimize a model by fitting to data consisting of multiple time points. The data can be fitted by applying a single scaling factor to the boolean simulation.

Usage

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gaBinaryDT(CNOlist, model, initBstring = NULL, sizeFac = 1e-04, NAFac = 1, popSize = 50,
pMutation = 0.5, maxTime = 60, maxGens = 500, stallGenMax = 100, selPress = 1.2,
elitism = 5, relTol = 0.1, verbose = TRUE, priorBitString = NULL, maxSizeHashTable = 5000,
boolUpdates, lowerB = lowerB, upperB = upperB)

Arguments

CNOlist

A CNOlist on which the score is based.

model

A model structure, as created by readSIF, normally pre-processed but that is not a requirement of this function.

initBstring

An initial bitstring to be tested, should be of the same size as the number of reactions in the model above (model$reacID). The default is all ones.

sizeFac

The scaling factor for the size term in the objective function, it defaults to 0.0001.

NAFac

The scaling factor for the NA term in the objective function, it defaults to 1.

popSize

The population size for the genetic algorithm, it is set to 50.

pMutation

the mutation probability for the genetic algorithm, default set to 0.5.

maxTime

the maximum optimisation time in seconds, default set to 60.

maxGens

The maximum number of generations in the genetic algorithm, default set to 500.

stallGenMax

The maximum number of stall generations in the genetic algorithm, default to 100.

selPress

The selective pressure in the genetic algorithm, default set to 1.2.

elitism

The number of best individuals that are propagated to the next generation in the genetic algorithm, default set to 5.

relTol

The relative tolerance for the best bitstring reported by the genetic algorithm, i.e., how different from the best solution, default set to 0.1.

verbose

Logical (default to TRUE): do you want the statistics of each generation to be printed on the screen?

priorBitString

At each generation, the GA algorithm creates a population of bitstrings that will be used to perform the optimisation. If the user knows the values of some bits, they can be used to overwrite bit values proposed by the GA algorithm. If provided, the priorBitString must have the same length as the initial bitstring and be made of 0, 1 or NA (by default, this bitstring is set to NULL, which is equivalent to setting all bits to NA). Bits that are set to 0 or 1 are used to replace the bits created by the GA itself (see example).

maxSizeHashTable

A hash table is used to store bitstrings and related scores. This allows the GA to be very efficient is the case of small models. The size of the hash table is 5000 by default, which may be too large for large models.

boolUpdates

The number of synchronous updates performed by the boolean simulator.

lowerB

The lower bound for the optimized value of the scaling factor.

upperB

The upper bound for the optimized value of the scaling factor.

Details

This is the modified version of gaBinaryT1 from the CellNOptR package, which is able to use all data from CNOlist$valueSignals.

Value

This function returns a list with elements:

bString

The best bitstring.

Results

A matrix with columns: "Generation", "Best_score", "Best_bitString", "Stall_Generation", "Avg_Score_Gen", "Best_score_Gen", "Best_bit_Gen", "Iter_time".

StringsTol

The bitstrings whose scores are within the tolerance.

StringsTolScores

The scores of the above-mentioned strings.

Author(s)

A. MacNamara

References

A. MacNamara, C. Terfve, D. Henriques, B. Penalver Bernabe and J. Saez-Rodriguez, 2012. State-time spectrum of signal transduction logic models. Physical biology, 9(4), p.045003.

See Also

getFitDT, simulatorDT

Examples

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library(CellNOptR)
library(CNORdt)
data(CNOlistPB, package="CNORdt")
data(modelPB, package="CNORdt")

# pre-process model
model = preprocessing(CNOlistPB, modelPB)

# optimise
initBstring <- rep(1, length(model$reacID))
opt1 <- gaBinaryDT(CNOlist=CNOlistPB, model=model, initBstring=initBstring,
verbose=TRUE, boolUpdates=10, maxTime=30, lowerB=0.8, upperB=10)

saezlab/CNORdt documentation built on Jan. 10, 2018, 12:57 a.m.