-------------------------------------------------------------------------------
-This example sets up the initial condtions like I did experimentally where
initially only one activator is present and the others are added after 30 m
-This example illustrates the use of multiple iterations of model.simulate
-This example also illustrates pulling out more concentrations for plotting
-This example compiles sequences without BTH domains
###############################################################################
'''
# G1 G2 G3 G4 G5 G6
act_vec = [1, 1, 2, 2, 3, 3]
blk_vec = [0, 0, 0, 0, 0, 0]
prod_vec = [-3, -2, -1, -3, -1, -2]
indc_vec = [0, 0, 0, 0, 0, 0]
''' initializing topology '''
TSNf = GGM.GeneletNetwork(act_vec, prod_vec, indc_vec, blk_vec)
TSNf.plot_topology()
# Define initial conditions
# dA1 dA2 dA3
dA_tot = np.array(
[250, 0,
0]) # total activator added (only dA2 is present to set that state)
# G1 G2 G3 G4 G5 G6
G_tot = np.array([50, 50, 50, 50, 50, 50]) # total genelet added
# initial genelet states (0 = OFF, 1 = ON, -1 = BLK)
# G1 G2 G3 G4 G5 G6
G_int_vec = [1, 1, 0, 0, 0, 0]
''' initializing initial conditions '''
TSNf.initial_conditions(
kgar = [5e3, 5e4, 5e5] # repression rates
kar = [1e4, 1e5, 1e6] # activator inhibition rates
kgb = [1e4, 1e5, 1e6] # free blocking rates
kgbc = [5e3, 5e4, 5e5] # coactivation rates
kbc = [1e4, 1e5, 1e6] # blocker inhibition rates
kgab = [5e3, 5e4, 5e5] # active blocking rates
kir = [1e4, 1e5, 1e6] # inducer binding rates
# OG Sam method of network definition
# G1 G2 G3
act_vec = [1, 2]
blk_vec = [0, 0]
prod_vec = [-2, 0]
indc_vec = [0, 0]
''' initializing topology '''
NN1 = GGM.GeneletNetwork(act_vec, prod_vec, indc_vec, blk_vec)
#NN1.plot_topology(show_rnas=0)
# Define initial conditions
# dA1 dA2 dA3
dA_tot = np.array([250, 250]) # total activator added
# G1 G2 G3
G_tot = np.array([15, 25]) # total genelet added
# initial genelet states (0 = OFF, 1 = ON, -1 = BLK)
# G1 G2
G_int_vec = [1, 1]
''' initializing initial conditions '''
NN1.initial_conditions(
dA_tot, G_tot, G_int_vec) # default of 0 for all other initial conditions
''' ############################################################################### Simulates the I_BS_IFFL1|2_FB1 network (Figure 5g) ------------------------------------------------------------------------------- ############################################################################### ''' # G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 act_vec = [1, 1, 2, 2, 3, 3, 4, 5, 6, 6, 7, 8, 9, 10] blk_vec = [0, 0, 0, 0, 3, 3, 4, 5, 6, 6, 7, 8, 9, 0] prod_vec = [-2,3,-1, 6, 4, 5,-5, 9, 7, 8, -8, 0, 0, 0] indc_vec = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, -1] ''' initializing topology ''' I_BS_IFFL_FB = GGM.GeneletNetwork(act_vec,prod_vec,indc_vec,blk_vec) # Define initial conditions # dA1 dA2 dA3 dA4 dA5 dA6 dA7 dA8 dA9 dA10 dA_tot = np.array([150, 250, 250, 250, 250, 250, 250, 250, 750, 0]) # total activator added # G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G_tot = np.array([25, 15, 50, 25, 5, 15, 35, 50, 5, 25, 35, 25, 175, 175]) # total genelet added # initial genelet states (0 = OFF, 1 = ON, -1 = BLK) # G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G_int_vec = [1, 1, 0, 0, -1,-1, -1,-1,-1,-1, -1, -1, -1, 0] rR_int = [0,1000,0,0,0,0,0,0,0,0] #dA_add = ['NA','NA','NA','NA','NA','NA','NA','NA','NA',250] ''' initializing initial conditions ''' I_BS_IFFL_FB.initial_conditions(dA_tot,G_tot,G_int_vec,dB_added=[0,0,250,50,150,150,150,150,0,0],rRin=rR_int) # default of 0 for all other initial conditions