Fig. 3
From: An efficient algorithm for identifying primary phenotype attractors of a large-scale Boolean network
Concatenation of local attractors. a The HPFP has three categories and five SCCs V 1,1, V 2,1, V 2,2, V 3,1 and V 3,2. Each arrow denotes the change from one state to another state at the next time step. The update rules for the network in Additional file 7. b There exist three attractors [10, 01], [00] and [11] in V 1,1. In this figure we consider the local attractors of subnetworks in the HPFP with starting signal [10, 01] generated from the two nodes x 1 and x 2. c V in2,1  = {x 1} denotes the set of nodes sending input signal into the SCC V 2,1 = {x 3, x 4}, where the input signal is 1,0 with period 2 and V 2,1 has a unique attractor [01, 00, 00, 10]. d V in2,2  = {x 2} and V 2,2 = {x 5, x 6, x 7}. The input signal coming from x 2 into V 2,2 is 0, 1 with period 2. The SCC V 2,2 has a unique attractor, which is cyclic with length 6. e V3,1 in = {x1, x3, x6} and V3,1 = {x8, x9}. The input signal coming from (x1, x3, x6) into V3,1 is cyclic with period 12. The SCC V3,1 has a unique attractor which is acyclic. f V 3,2 in = {x7} and V3,2 = {x10, x11}. The input signal coming from x7 into V3,2 is cyclic with period 6. The SCC V3,2 has a unique attractor which is acyclic. g Table for all the local attractors of subnetworks in the HPFP with the signal [10, 01] in V 1,1. The second column denotes the local attractor [10, 01] of V 1,1 = {x 1, x 2}, where each state in the attractor has its position denoted by the order in the first column. h Sequential concatenation of the local attractors in the Table. This yields the unique global attractor of the HPFP, which is cyclic with a period of 12 and has the local attractor 〚10, 01〛 in V 1,1