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

This function is the genetic algorithm to be used to optimise a model by fitting to data containing one time point. It is the function gaBinaryT1 of CellNOptR modified in orter to differently weights for the integrated links

1 2 3 4 | ```
gaBinaryT1W(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)
``` |

`CNOlist` |
a CNOlist on which the score is based (based on valueSignals[[2]], i.e. data at time 1) |

`model` |
a model structure, as created by |

`initBstring` |
an initial bitstring to be tested, should be of the same size as the number of reactions in the model above (model$reacID). Default is all ones. |

`sizeFac` |
the scaling factor for the size term in the objective function, default to 0.0001 |

`NAFac` |
the scaling factor for the NA term in the objective function, default to 1 |

`popSize` |
the population size for the genetic algorithm, default 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 use to store bitstring and related score. 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. |

The whole procedure is described in details in Saez-Rodriguez et al. (2009). The basic principle is that at each generation, the algorithm evaluates a population of models based on excluding or including some gates in the initial pre-processed model (this is encoded in a bitstring with contains 0/1 entries for each gate). The population is then evolved based on the results of the evaluation of these networks, where the evaluation is obtained by simulating the model (to steady state) under the various conditions present in the data, and then computing the squared deviation from the data, to which a penalty is added for size of the model and for species in the model that do not reach steady state.

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 |

C. Terfve, T. Cokelaer, F.Eduati

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.

gaBinaryT1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
data(CNOlistDREAM,package="CellNOptR")
data(DreamModel,package="CellNOptR")
model<-preprocessing(data=CNOlistDREAM, model=DreamModel)
BTable <- makeBTables(CNOlist=CNOlistDREAM, k=2, measErr=c(0.1, 0))
modelIntegr <- mapBTables2model(BTable=BTable,model=model,allInter=TRUE)
modelIntegrWeight <- weighting(modelIntegr=modelIntegr, PKNmodel=DreamModel,
CNOlist=CNOlistDREAM, integrFac=10)
initBstring<-rep(1,length(modelIntegr$reacID))
# training to data using genetic algorithm (run longer to obtain better results)
DreamT1opt<-gaBinaryT1W(
CNOlist=CNOlistDREAM,
model=modelIntegrWeight,
initBstring=initBstring,
maxGens=2,
popSize=5,
verbose=FALSE)
``` |

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