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R: Perform parameter estimation using a genetic algorithm...

parEstimationLBodeGA

R Documentation

Perform parameter estimation using a genetic algorithm (package genalg).

Description

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

Usage

	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, initial_state=0.1)

Arguments

`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.
`initial_state`	initial state of the dynamic nodes (non-measured) (Defaults to 0.1)

Value

`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).

Author(s)

David Henriques, Thomas Cokelaer

Examples

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);

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(CNORode)
Loading required package: CellNOptR
Loading required package: RBGL
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: hash
hash-2.2.6 provided by Decision Patterns

Loading required package: ggplot2
Loading required package: RCurl
Loading required package: bitops
Loading required package: Rgraphviz
Loading required package: grid
Loading required package: XML

Attaching package: 'XML'

The following object is masked from 'package:graph':

    addNode

Loading required package: genalg

Attaching package: 'CNORode'

The following object is masked from 'package:stats':

    simulate

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/CNORode/parEstimationLBodeGA.Rd_%03d_medium.png", width=480, height=480)
> ### Name: parEstimationLBodeGA
> ### Title: Perform parameter estimation using a genetic algorithm (package
> ###   genalg).
> ### Aliases: parEstimationLBodeGA
> ### Keywords: genetic algorithm CellNOptR logic model
> 
> ### ** Examples
> 
> 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);
[1] "Iteration:1  Best_f:0.0854559767108977  N_Evals:10"
[1] "Iteration:2  Best_f:0.0854559767108977  N_Evals:20"
[1] "Iteration:3  Best_f:0.0854559767108977  N_Evals:30"
[1] "Iteration:4  Best_f:0.0254022123367672  N_Evals:40"
[1] "Iteration:5  Best_f:0.0254022123367672  N_Evals:50"
[1] "Iteration:6  Best_f:0.0219997461561912  N_Evals:60"
[1] "Iteration:7  Best_f:0.0219997461561912  N_Evals:70"
[1] "Iteration:8  Best_f:0.0219997461561912  N_Evals:80"
[1] "Iteration:9  Best_f:0.0219997461561833  N_Evals:90"
[1] "Iteration:10  Best_f:0.0184457218337944  N_Evals:100"
[1] "Iteration:11  Best_f:0.0184457218337944  N_Evals:110"
[1] "Iteration:12  Best_f:0.0183427827639755  N_Evals:120"
[1] "Iteration:13  Best_f:0.0178215204078944  N_Evals:130"
[1] "Iteration:14  Best_f:0.0176272853949692  N_Evals:140"
[1] "Iteration:15  Best_f:0.0167420314449053  N_Evals:150"
[1] "Iteration:16  Best_f:0.0153204665485795  N_Evals:160"
[1] "Iteration:17  Best_f:0.0120946654642356  N_Evals:170"
[1] "Iteration:18  Best_f:0.0119692108374706  N_Evals:180"
[1] "Iteration:19  Best_f:0.0116108974553988  N_Evals:190"
[1] "Iteration:20  Best_f:0.0114393314914684  N_Evals:200"
[1] "Iteration:21  Best_f:0.0113493874117593  N_Evals:210"
[1] "Iteration:22  Best_f:0.010237615300133  N_Evals:220"
[1] "Iteration:23  Best_f:0.010237615300133  N_Evals:230"
[1] "Iteration:24  Best_f:0.00997208674644057  N_Evals:240"
[1] "Iteration:25  Best_f:0.00916567374787542  N_Evals:250"
[1] "Iteration:26  Best_f:0.00916567374787542  N_Evals:260"
[1] "Iteration:27  Best_f:0.00868173441364238  N_Evals:270"
[1] "Iteration:28  Best_f:0.00840524941393896  N_Evals:280"
[1] "Iteration:29  Best_f:0.00805265199842338  N_Evals:290"
[1] "Iteration:30  Best_f:0.00781742746453421  N_Evals:300"
[1] "Iteration:31  Best_f:0.00780973264265596  N_Evals:310"
[1] "Iteration:32  Best_f:0.00729208903129881  N_Evals:320"
[1] "Iteration:33  Best_f:0.00726454724972479  N_Evals:330"
[1] "Iteration:34  Best_f:0.00699463947073246  N_Evals:340"
[1] "Iteration:35  Best_f:0.00661727244739602  N_Evals:350"
[1] "Iteration:36  Best_f:0.00588047884973334  N_Evals:360"
[1] "Iteration:37  Best_f:0.00588047884973334  N_Evals:370"
[1] "Iteration:38  Best_f:0.00582771313144051  N_Evals:380"
[1] "Iteration:39  Best_f:0.00582771313144051  N_Evals:390"
[1] "Iteration:40  Best_f:0.00581611279934521  N_Evals:400"
> 
> #Visual solution after optimization
> simulatedData=plotLBodeFitness(cnolistCNORodeExample, model,indices=indices,ode_parameters=ode_parameters);
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>