Compute TES

Creates a structure for constructing the TES as described in "A Dynamical Model of Genetic Networks for Cell Differentiation Villani M, Barbieri A, Serra R (2011) A Dynamical Model of Genetic Networks for Cell Differentiation. PLOS ONE 6(3): e17703. https://doi.org/10.1371/journal.pone.0017703"

Usage

getTESs(ATM)

Arguments

ATM

ATM structure as returned from the getATM method.

Value

The output will be a named list that contains the list of computed TESs, the noise thresholds at which they emerged and lastly the ATM structure.

Examples

net <- BoolNet::generateRandomNKNetwork(10, 2)
attractors <- BoolNet::getAttractors(net)
ATM <- getATM(net, attractors)
getTESs(ATM)
#> $TES
#> $TES$level_0
#> $TES$level_0$TES_1
#> [1] "a1" "a2" "a3"
#> 
#> 
#> $TES$level_1
#> $TES$level_1$TES_2
#> [1] "a3"
#> 
#> 
#> $TES$level_2
#> $TES$level_2$TES_3
#> [1] "a3"
#> 
#> 
#> $TES$level_3
#> $TES$level_3$TES_4
#> [1] "a3"
#> 
#> 
#> $TES$level_4
#> $TES$level_4$TES_5
#> [1] "a1"
#> 
#> $TES$level_4$TES_6
#> [1] "a3"
#> 
#> 
#> $TES$level_5
#> $TES$level_5$TES_7
#> [1] "a1"
#> 
#> $TES$level_5$TES_8
#> [1] "a2"
#> 
#> $TES$level_5$TES_9
#> [1] "a3"
#> 
#> 
#> $TES$level_6
#> $TES$level_6$TES_10
#> [1] "a1"
#> 
#> $TES$level_6$TES_11
#> [1] "a2"
#> 
#> $TES$level_6$TES_12
#> [1] "a3"
#> 
#> 
#> 
#> $thresholds
#> [1] 0.00 0.10 0.20 0.25 0.65 0.70 0.90
#> 
#> $ATM
#> $ATM$ATM
#>      a1  a2   a3
#> a1 0.25 0.1 0.65
#> a2 0.10 0.2 0.70
#> a3 0.10 0.0 0.90
#> 
#> $ATM$lostFLips
#> [1] 0
#> 
#> $ATM$attractors
#> $ATM$attractors$decimal
#> $ATM$attractors$decimal$a1
#> $ATM$attractors$decimal$a1$involvedStates
#>      [,1] [,2]
#> [1,]  132 1015
#> 
#> $ATM$attractors$decimal$a1$basinSize
#> [1] 14
#> 
#> 
#> $ATM$attractors$decimal$a2
#> $ATM$attractors$decimal$a2$involvedStates
#>      [,1] [,2]
#> [1,]  140  919
#> 
#> $ATM$attractors$decimal$a2$basinSize
#> [1] 10
#> 
#> 
#> $ATM$attractors$decimal$a3
#> $ATM$attractors$decimal$a3$involvedStates
#>      [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,]    4  999  164 1013  196  758
#> 
#> $ATM$attractors$decimal$a3$basinSize
#> [1] 1000
#> 
#> 
#> 
#> $ATM$attractors$binary
#> $ATM$attractors$binary$a1
#>        [,1] [,2]
#> Gene1     0    1
#> Gene2     0    1
#> Gene3     1    1
#> Gene4     0    0
#> Gene5     0    1
#> Gene6     0    1
#> Gene7     0    1
#> Gene8     1    1
#> Gene9     0    1
#> Gene10    0    1
#> 
#> $ATM$attractors$binary$a2
#>        [,1] [,2]
#> Gene1     0    1
#> Gene2     0    1
#> Gene3     1    1
#> Gene4     1    0
#> Gene5     0    1
#> Gene6     0    0
#> Gene7     0    0
#> Gene8     1    1
#> Gene9     0    1
#> Gene10    0    1
#> 
#> $ATM$attractors$binary$a3
#>        [,1] [,2] [,3] [,4] [,5] [,6]
#> Gene1     0    1    0    1    0    0
#> Gene2     0    1    0    0    0    1
#> Gene3     1    1    1    1    1    1
#> Gene4     0    0    0    0    0    0
#> Gene5     0    0    0    1    0    1
#> Gene6     0    1    1    1    0    1
#> Gene7     0    1    0    1    1    1
#> Gene8     0    1    1    1    1    1
#> Gene9     0    1    0    1    0    0
#> Gene10    0    1    0    1    0    1
#> 
#> 
#> 
#>