parEstimationLBodeGA: Perform parameter estimation using a genetic algorithm...
In CNORode: ODE add-on to CellNOptR

Description Usage Arguments Value Author(s) See Also Examples

This function uses a genetic algorithm (package genalg) to perform parameter estimation. The objective function is the same as the one provided by getLBodeContObjFunction.

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	parEstimationLBodeGA(cnolist, model, ode_parameters = NULL, indices = NULL, mutationChance = NA, popSize = 200, iters = 100, 
		elitism = NA, time = 1, monitor = TRUE, verbose = 0, transfer_function = 3, reltol = 1e-04, 
		atol = 0.001, maxStepSize = Inf, maxNumSteps = 1e+05, maxErrTestsFails = 50, nan_fac = 1)

`cnolist`	A list containing the experimental design and data.
`model`	The logic model to be simulated.
`ode_parameters`	A list with the ODEs parameter information. Obtained with `createLBodeContPars`.
`indices`	Indices to map data in the model. Obtained with indexFinder function from CellNOptR.
`mutationChance`	the chance that a gene in the chromosome mutates. By default 1/(size+1). It affects the convergence rate and the probing of search space: a low chance results in quicker convergence, while a high chance increases the span of the search space.
`popSize`	the population size.
`iters`	the number of iterations.
`elitism`	the number of chromosomes that are kept into the next generation. By default is about 20% of the population size
`time`	An integer with the index of the time point to start the simulation. Default is 1.
`monitor`	If TRUE a plot will be generated to monitor the objective function
`verbose`	A logical value that triggers a set of comments.
`transfer_function`	The type of used transfer. Use 1 for no transfer function, 2 for Hill function and 3 for normalized Hill function.
`reltol`	Relative Tolerance for numerical integration.
`atol`	Absolute tolerance for numerical integration.
`maxStepSize`	The maximum step size allowed to ODE solver.
`maxNumSteps`	The maximum number of internal steps between two points being sampled before the solver fails.
`maxErrTestsFails`	Specifies the maximum number of error test failures permitted in attempting one step.
`nan_fac`	A penalty for each data point the model is not able to simulate. We recommend higher than 0 and smaller that 1.

`LB_n`	A numeric value to be used as lower bound for all parameters of type n.
`LB_k`	A numeric value to be used as lower bound for all parameters of type k.
`LB_tau`	A numeric value to be used as lower bound for all parameters of type tau.
`UB_n`	A numeric value to be used as upper bound for all parameters of type n.
`UB_k`	A numeric value to be used as upper bound for all parameters of type k.
`UB_tau`	A numeric value to be used as upper bound for all parameters of type tau.
`default_n`	The default parameter to be used for every parameter of type n.
`default_k`	The default parameter to be used for every parameter of type k.
`default_tau`	The default parameter to be used for every parameter of type tau.
`LB_in`	An array with the the same length as ode_parameters$parValues with lower bounds for each specific parameter.
`UB_in`	An array with the the same length as ode_parameters$parValues with upper bounds for each specific parameter.
`opt_n`	Add all parameter n to the index of parameters to be fitted.
`opt_k`	Add all parameter k to the index of parameters to be fitted.
`opt_tau`	Add all parameter tau to the index of parameters to be fitted.
`random`	A logical value that determines that a random solution is for the parameters to be optimized.
`res`	A list containing the information provided by the nonlinear optimization solver (genalg).

David Henriques, Thomas Cokelaer

CellNOptR createLBodeContPars rbga

data("ToyCNOlist",package="CNORode");
data("ToyModel",package="CNORode");
data("ToyIndices",package="CNORode");
	
ode_parameters=createLBodeContPars(model,random=TRUE);
#Visualize intial simulation
#simulatedData=plotLBodeFitness(cnolistCNORodeExample, model,ode_parameters,indices=indices)

ode_parameters=parEstimationLBodeGA(cnolistCNORodeExample,model,ode_parameters=ode_parameters,
indices=indices,maxStepSize=1,atol=1e-3,reltol=1e-5,transfer_function=2,popSize=10,iter=40);

#Visual solution after optimization
simulatedData=plotLBodeFitness(cnolistCNORodeExample, model,indices=indices,ode_parameters=ode_parameters);