# Boolean network model of the control of T-helper cell differentiation from # "A method for the generation of standardized qualitative dynamical systems # of regulatory networks", L. Mendoza and I. Xenarios # J. Theor. Biol. and Medical Modelling, 2006, vol. 3, no. 13 #total number of nodes .v 23 # labels of nodes and names of corresponding components # 1 = TCR # 2 = NFAT # 3 = IFN-\beta # 4 = IFN-\beta R # 5 = IL-18 # 6 = IL-18R # 7 = IRAK # 8 = SOCS1 # 9 = IL-12 # 10 = IL-12R # 11 = STAT4 # 12 = T-bet # 13 = IFN-\gamma # 14 = IFN-\gamma R # 15 = JAK1 # 16 = STAT1 # 17 = IL-4 # 18 = IL-4R # 19 = STAT6 # 20 = GATA3 # 21 = IL-10 # 22 = IL-10R # 23 = STAT3 # As a result of simulation, we get the following 3 single-point attractors # corresponding to cell types Th-0, Th-1 and Th-2: # Th0: all-0 # Th1: 00000001000111000000000 # Th2: 00000000000000001111111 # 1 = TCR .n 1 0 # 2 = NFAT .n 2 1 1 1 1 # 3 = IFN-\beta .n 3 0 # 4 = IFN-\beta R .n 4 1 3 1 1 # 5 = IL-18 .n 5 0 # 6 = IL-18R .n 6 2 5 19 10 1 # 7 = IRAK .n 7 1 6 1 1 # 8 = SOCS1 .n 8 2 12 16 1- 1 -1 1 # 9 = IL-12 .n 9 0 # 10 = IL-12R .n 10 2 9 19 10 1 # 11 = STAT4 .n 11 2 10 20 10 1 # 12 = T-bet .n 12 3 12 16 20 1-0 1 -10 1 # 13 = IFN-\gamma .n 13 5 2 7 11 12 23 1---0 1 -1--0 1 --1-0 1 ---10 1 # 14 = IFN-\gamma R .n 14 1 13 1 1 # 15 = JAK1 .n 15 2 14 8 10 1 # 16 = STAT1 .n 16 2 4 15 1- 1 -1 1 # 17 = IL-4 .n 17 2 20 16 10 1 # 18 = IL-4R .n 18 2 17 8 10 1 # 19 = STAT6 .n 19 1 18 1 1 # 20 = GATA3 .n 20 3 19 20 12 1-0 1 -10 1 # 21 = IL-10 .n 21 1 20 1 1 # 22 = IL-10R .n 22 1 21 1 1 # 23 = STAT3 .n 23 1 22 1 1